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Learn MoreMastering Voice AI : From ASR to Emotion AI to Voice Cloning
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Mastering Voice AI : From ASR to Emotion AI to Voice Cloning
Master cutting-edge SpeechLMs and build next-generation voice AI applications with end-to-end speech capabilities
Created byVinit Singh
Last updated 10/2026
English
English [Auto],Korean [Auto],
What you'll learn
- Develop end-to-end speech language models using Python and Transformer architectures.
- Master audio feature extraction and tokenization for speech recognition and synthesis.
- Build AI for emotion recognition and personalized speech with real-world applications.
- Evaluate SpeechLMs with metrics like WER and explore ethical AI design practices.
Explore related topics
Course content
8 sections • 141 lectures • 19h 40m total length
- Introduction1:59
- Introduction to Module 1 -Intro to Speech LP and the Emergence of SpeechLM Model3:01
Explore the shift from traditional modular speech processing to end-to-end speech language models, and discover their advantages, limitations, and practical coding exercises across module 1.
- Intro 1.1 Overview of Traditional Speech Processing1:15
- 1111 Overview of Traditional Speech Processing5:15
- 1112 Automatic Speech Recognition (ASR)6:07
- 1113 Natural Language Understanding (NLU)2:09
- 1114 Text-to-Speech (TTS)Text-to-Speech (TTS)3:33
- 1115 Pipeline Integration & Characteristics1:42
- 1116 Traditional vs. Modern Approaches2:25
- Summary 1.1 Overview of Traditional Speech Processing1:27
- How to download Anaconda and create environment3:04
- 1.1 Coding Eg & Ex. Discussion - Building a Speech-Enabled Conversational Agent9:35
There is a coding example and a coding exercise . Here we implement:
Code example - This code demonstrates a sequential voice assistant pipeline with three decoupled modules: Automatic Speech Recognition (ASR), Large Language Model (LLM) processing, and Text-to-Speech (TTS) synthesis
Code exercise - This script demonstrates a multi-turn speech-based conversational pipeline using Automatic Speech Recognition (ASR), a Large Language Model (LLM), and Text-to-Speech (TTS) synthesis. It handles three user interactions, maintains conversation history, and saves a summary.
Key Libraries Used:
speech_recognition: Captures microphone input and performs ASR via Google's API
transformers: Provides the pipeline API for text generation using GPT-2
gTTS: Converts text responses to synthetic speech
playsound: Plays generated audio responses
- Quiz - 1.1 Overview of Traditional Pipeline
- Intro 1.2 Limitations of the Traditional Pipeline1:33
- 1211 Limitation 1 Information Loss During Modality Conversion5:21
- 1211 Limitation 1 Information Loss During Modality Conversion5:21
- 1212 Limitation 2 Significant Latency2:16
- 1213 Limitation 3 Error Accumulation (Error Propagation)3:16
- 1214 Synergy of Limitations3:36
- 1.2 Coding Example Discussion - Speech Pipeline with Simulated Limitations8:07
Speech Pipeline with Simulated Limitations
This script demonstrates a speech-to-speech conversational pipeline with explicit latency measurement and error simulation. It compares normal operation against scenarios with ASR information loss and transcription errors to highlight real-world system limitations.
Key Libraries Used:
speech_recognition: Audio capture and Google ASR integration
openai: Access to GPT-4o-mini LLM via API
gtts: Text-to-speech conversion
dotenv: Secure API key management
time: Precise latency measurement
random: Probabilistic error injection
- Post Lec 1.21:56
- Quiz - 1.2 Limitations of Traditional Pipeline
- Intro 1.3 to Speech Language Models (SpeechLMs)1:37
- 1311 What are SpeechLMs – Definition & their functioning in brief3:35
- 1312 How do SpeechLMs work The Core Idea4:18
- 1313 How do SpeechLMs capture Rich Information2:52
- 1314 Addressing Traditional Limitations2:03
- 1315 Challenges with SpeechLMs3:00
- Post-Lect 1.31:49
- Coding Eg & Ex Disc. 1.3- Audio Tokenization and Reconstruction + Multi-Bandwidt9:57
Coding Example - Audio Tokenization and Reconstruction with EnCodec
This script demonstrates audio compression using EnCodec's neural codec, converting speech to discrete tokens and reconstructing it. It highlights how SpeechLMs operate on tokenized representations rather than raw waveforms.
Coding Exercise 1 - Multi-Bandwidth Audio Tokenization Study
This script demonstrates how audio quality varies with compression bandwidth using EnCodec's neural codec. It processes input audio at different bitrates (1.5, 6, 12 kbps) to showcase compression artifacts and tokenization efficiency.
Coding Exercise 2 - Neural Audio Codec Bandwidth Comparison¶
This script demonstrates the impact of compression bandwidth on audio quality using EnCodec's neural codec. It processes input audio at multiple bitrates (1.5, 6, 12 kbps) to showcase the quality-compression tradeoff inherent in token-based speech representations.
Key Libraries Used:
encodec: Core neural codec for tokenization/reconstruction
torch/torchaudio: Audio tensor processing and resampling
soundfile (sf): Audio file I/O operations
numpy (np): Signal generation and math operations
os: File system management
- Quiz - 1.3 Introduction to Speech Language Models (SpeechLMs)
- Intro 1.4 Advantages of Speech Language Models (SpeechLMs)2:11
- 1411 1. Reduced Latency3:56
- 1412 2. Potential for Better Handling of Paralinguistic Information3:20
- 1413 3. Applicability to Languages with More Spoken Than Written Content1:54
- 1414 Challenges and Considerations3:27
- Coding Eg & Ex 1.4 - Speech & Emotion Recognition with SpeechLM - wav2vec29:26
Demonstrate asr with wav2vec2 and transformers, processing mono 16kHz audio on gpu or cpu for transcription. Show emotion recognition using the same model, highlighting multitask capabilities and softmax-based classification.
- Quiz - 1.4 Advantages of Speech Language Models (SpeechLMs)
- Intro 1.5 SpeechLMs vs LLMs2:31
- 1511 1. Input Modality3:01
- 1512 2. Output Modality1:28
- 1513 3. Architecture3:37
- 1514 4. Internal Representations3:54
- 1515 5. Training Data1:55
- Post-Lecture 1.52:58
- 1517 7. Modern Trends2:45
- Coding Example Discussion 1.5 - TextLM vs. SpeechLM Modality Comparison4:25
Compare text language models and speech language models through a hands-on coding demo, exploring text versus audio inputs, ASR transcription with whisper tiny, and GPT-2 and GPT-4 mini outputs.
- Quiz - 1.5 Contrast of SpeechLM with Text-based Language Models (TextLMs)
- Intro 1.6 Applications of SpeechLMs2:13
- 1611 Applications of SpeechLMs Introduction2:56
- 1612 1. Semantic Tasks (Focus on ContentMeaning)5:13
- 1613 2. Speaker-Related Tasks (Focus on Who is Speaking)4:04
- 1614 3. Paralinguistic Tasks (Focus on How Something is Said)3:22
- 1615 Broader Impact2:57
- Summary 1.6 Applications of Speech Language Models (SpeechLMs)1:58
- Coding Example Discussion 1.6 - Emotion-Aware Speech Assistant4:05
Explore emotion-aware speech assistants by combining ASR and emotion detection using whisper tiny and wave two vec, and generate responses based on semantic meaning and detected emotion.
- Quiz - 1.6 Applications of Speech Language Models (SpeechLMs)
- Intro to Module 2 - Fundamentals of Speech and Language for SpeechLMs3:15
Explore the fundamentals of sound, including amplitude and frequency, the source-filter model, phonetics and phonology, phoneme mapping, and analog-to-digital representation with mfcc, plus cross-modal alignment.
- 2.1 Basics of Speech Acoustics - 1 Sound Waves,Waveform , Frequency,Spectrum12:43
ØSound Waves
ØKey Properties of Sound Waves:
• Amplitude
• Frequency
ØRepresenting & Visualizing Sound Digitally – Waveform , Spectrum
- 2.1 Basics of Speech - 2 Spectrograms, MFCCs, Applications in Deep Learning13:50
ØRepresenting & Visualizing Sound Digitally – Spectrograms
ØOther Representations:
o Waveform
o Mel-Frequency Cepstral Coefficients (MFCCs)
o Fourier Transform
o Mel Spectrograms
ØApplications in Deep Learning
ØChallenges and Considerations
ØSpeechLM Solutions
ØSummary & Key Takeaways
- Code Eg & Ex 2.1 - Speech Analysis & Transcription + Speech Feature Extraction5:34
- Quiz 2.1 Basics of Speech Acoustics
- 2.2 The Source-Filter Model of Speech Production - 1.The Source ,2.The Filter9:31
ØThe Source-Filter Model of Speech Production - Introduction
ØComponents of the Model - 1.The Source
ØComponents of the Model - 2.The Filter (Vocal Tract)
- 2.2 The Source-Filter Model - 2 Speech Output, Key Concepts of Speech,Relevance9:20
ØSpeech Output
Ø Key Concepts of Speech
ØRelevance to Speech Processing
ØChallenges and Considerations
ØSummary & Key Takeaways
- Quiz 2.2 The Source-Filter Model of Speech Production
- 2.3 Phonetics and Phonology in Speech - 1 Phones, Phonemes, and Allophones13:59
ØPhones, Phonemes, and Allophones
ØPhonetics and Phonology in Speech
ØPhonetics: The Study of Speech Sounds
ØPhonetic Features
ØPhonology: The Sound System of a Language
- 2.3 Phonetics and Phonology - 2 Mapping Sounds to Phonemes and Phonetic Features10:56
Map sounds to phonemes through formants and voice onset time using acoustic cues and features, with CNN or transformer models and lexicon lookups, handling coarticulation and variations.
- Code Eg Discussion - 2.3 Phonetic Recognition and Analysis System5:29
Explore a coding example that records audio, performs ASR to convert words to phonemes, maps phonemes to phonetic features, and plots a spectrogram using CMU dict, NLTK, and Librispeech models.
- Quiz 2.3 - Phonetics and Phonology in Speech
- 2.4 Audio Feature Extraction - 1 Mel Frequency Cepstral Coefficients (MFCCs)8:51
ØAudio Feature Extraction - Introduction
ØTraditional Feature Extraction: Mel Frequency Cepstral Coefficients (MFCCs)
- 2.4 Audio Feature Extraction - 2 Raw Waveforms and Learned Audio Representations11:23
ØModern Approaches in SpeechLMs: Raw Waveforms and Learned Audio Representations
ØComparison: Traditional Feature Extraction vs Modern Approaches in SpeechLMs
ØChallenges and Considerations
ØSummary & Key Takeaways
- Coding Eg Discussion 2.4 - Noise Robustness in Speech Feature Analysis6:29
- Quiz 2.4 Audio Feature Extraction
- 2.5 Cross-Modal Representation SpeechLMs - 1 1.Audio Representation 2. Text Rep11:57
ØCross-Modal Representations for Speech Language Models - Introduction
ØComponents of Cross-Modal Representations
Ø1. Audio Representations
Ø2. Text Representations
- 2.5 Cross-Modal - 2 3. Cross-Modal Alignment, Relevance to SpeechLMs, Implement13:13
Ø 3. Cross-Modal Alignment
ØRelevance to SpeechLMs
ØPractical Considerations
Ø Notes on Implementation
ØChallenges and Considerations
ØSummary & Key Takeaways
- Code Eg & Ex 2.5 - Cross-Modal Alignment Visualization & Analysis Framework6:45
Explore cross-modal representation in speech language models by aligning acoustic features with text using whisper, mFCC, mel spectrogram, and Bert tokenizer. Illustrate simple uniform time alignment and initial cross-modal visualization.
- Quiz 2.5 - Cross-Modal Representations for SpeechLMs
- Introduction to Module 3 - Architectures and Key Components of SpeechLMs2:34
- 3.1 General Architecture SpeechLM - Intro. 1 Speech Tokenizer 2.Language Model13:11
ØGeneral Architecture of a SpeechLM - Introduction
Ø1.Speech Tokenizer (or Acoustic Encoder)
Ø2.Language Model (LM) on Audio Tokens/Representations
- 3.1 Architecture SpeechLM - 2 Token-to-Speech Synthesizer (Vocoder), Co-ordinati10:42
Ø3.Token-to-Speech Synthesizer (Vocoder)
ØHow they work together - Key Considerations
ØChallenges and Considerations
ØSummary & Key Takeaways
- Code Eg & Ex 3.1 - Simplified SpeechLM Pipeline Simulation + w/ Bigram Language7:41
Explore a simplified speech language model pipeline with synthetic speech, eight-token k-means tokenization, and bigram language modeling, plus a mock LLM, vocoder, and regenerated audio.
- Quiz 3.1 General Architecture of a SpeechLM
- 3.2 Speech Tokenizers - 1 Audio Tokenization Methods: 1. Audio Codec Models14:51
Explore how speech tokenizers convert high-rate audio into discrete tokens or continuous embeddings, enabling transformers in ASR, TTS, and speech translation, audio encoding based and self-supervised techniques and concatenation strategies.
- 3.2 Speech - 2 2. Self-Supervised Learning (SSL) 3.Other Methods10:21
2. Self-Supervised Learning (SSL) Tokens/Representations
3. Other Methods
ØConcatenating Different Types of Audio Tokens
ØKey Considerations
ØChallenges
ØSummary & Key Takeaways
- Code Eg & Ex - Speech Tokenization(ST) Method Comparison + ST with Enhancd Vocab9:22
Compare codec-based and self-supervised tokenization, and explore a hybrid concatenation approach using K-means. Analyze how vocabulary size and discrete versus continuous tokens impact tokenization in synthetic speech.
- Quiz 3.2 Speech Tokenizers
- 3.3 Language Models in SLMs - 1 Transformer Architecture, Autoregressive Predn14:03
ØLanguage Models in SpeechLMs - Introduction
ØTransformer Architecture as the Backbone
ØAutoregressive Prediction of Audio Tokens
- 3.3 Language Models - 2 Adaptation Text-Based LLMs for Speech, Multi-Stream LM13:40
ØAdaptation of Text-Based LLMs for Speech
ØMulti-Stream Language Model Implementations
ØKey Considerations
ØChallenges
ØSummary & Key Takeaways
- Code Eg & Ex - Transformer-Based Speech Token Prediction + Speech Token Modeling9:00
- Quiz 3.3 Language Models in SpeechLMs
- 3.4 Vocoders in SpeechLMs -Intro 1 Function of the Vocoder , Why is it needed?8:45
ØThe Vocoder in Text-to-Speech Synthesis - Introduction
ØExplanation: Function of the Vocoder
ØWhy is it needed?
- 3.4 Vocoders - 2 MelGAN, HiFi-GAN, WaveNet7:48
ØExamples of Vocoders
ØKey Considerations
ØChallenges
ØSummary & Key Takeaways
- Code Eg & Ex 3.4 - Neural Vocoder for Audio Synthesis + Griffin-Lim Algorithm11:58
Examine the vocoder component of speech language models by training a neural network to convert mel spectrograms to audio, and compare Griffin limb algorithm and convolutional vocoders through practical exercises.
- Quiz 3.4 Vocoders in SpeechLMs
- Introduction to Module 4 - Training Methodologies for SpeechLMs3:28
- 4.1 Training Stages for SpeechLMs - Intro., 1 Training Pipeline, 1. Pre-Training14:13
ØOverview of Training Stages for SpeechLMs - Introduction
ØTraining Pipeline for Generative SpeechLMs
Ø1. Pre-Training
- 4.1 Training Stages - 2 2. Instruction-Tuning, 3. Post-Alignment, Key Conside15:59
Ø2. Instruction-Tuning
Ø3. Post-Alignment
ØKey Considerations
ØChallenges
ØSummary & Key Takeaways
- Code Eg & Ex - Multi-Stage Training for SpeechLM + Comprehensive Trainig Pipline9:03
- Quiz 4.1 Overview of Training Stages for SpeechLMs
- 4.2 Pre-Training SpeechLMs - 1 Large-Scale Speech Data, Commonly Used Datasets17:31
ØPre-Training Methodologies for SpeechLMs - Introduction
ØImportance of Large-Scale Speech Data for Pre-Training
ØCommonly Used Datasets for Pre-Training SpeechLMs
ØRole of Paired Speech-Text Datasets
- 4.2 Pre-Training SpeechLMs - 2 Paired Speech-Text Datasets, Joint Pre-training19:29
ØDatasets with Paired Speech and Text Transcripts
ØMethods of Modeling Speech and Text Tokens During Pre-Training
ØJoint Pre-training Objectives & Architectures
ØKey Considerations
ØChallenges
ØSummary & Key Takeaways
- Code Eg & Ex - Lightweight SpeechLM Pre-Training + Advanced Decoding Strategies11:12
Demonstrate a mask-token pre-training setup for a lightweight speech language model with 32-d embeddings, a 10-token vocabulary, and one transformer layer, plus greedy decoding and varied decoding strategies.
- 4.2 Quiz Pre-Training Methodologies for SpeechLMs
- 4.3 Instruction-Tuning SpeechLMs - 1 Understanding Instruction-Tuning, Process16:10
ØInstruction-Tuning for Speech Language Models (SpeechLMs) - Introduction
ØUnderstanding Instruction-Tuning for SpeechLMs
ØInstruction-Tuning Process
- 4.3 Instruction-Tuning 2 Creating Effective Datasets, (PEFT) Techniques: LoRA17:51
ØCreating Effective Datasets
ØParameter-Efficient Fine-Tuning (PEFT) Techniques: LoRA
ØBenefits of using LoRA for Instruction-Tuning SpeechLMs
ØComparison of Fine-Tuning Techniques
ØChallenges
ØSummary & Key Takeaways
- Codes 4.2- PEFT of Wav2Vec2 with LoRA + Instruction-Based Speech Recog Tuning10:44
- Quiz 4.3 Instruction-Tuning for Speech Language Models (SpeechLMs)
- 4.4 Post-Alignment Techniques - Introduction 1 Understanding Post-Alignment13:53
ØPost-Alignment Techniques for SpeechLMs - Introduction
ØUnderstanding Post-Alignment in SpeechLMs
- 4.4 Post-Alignment Techniques - 2 RLHF, DPO, Safety Patches, Adversarial, RAG14:26
ØTechniques to Align the Language Model's Output
ØComparison of Post-Alignment Techniques
ØDesired Distribution of Tokens
ØSafety Risks Associated with SpeechLMs
ØHow Post-Alignment Mitigates These Risks
ØChallenges and Considerations for SpeechLMs
ØSummary & Key Takeaways
- Codes 4.4 - Real-World SpeechLM Deployment with Post-Alignment Techniques9:35
Explore real-world speech language model deployment using post-alignment techniques, including safety filters, keyword filtering, and off-token biasing, to control output behavior and domain-specific vocabulary.
- 4.4 Quiz Post-Alignment Techniques for Speech Language Models (SpeechLMs)
- Introduction to Module 5 - Capabilities and Applications of SpeechLMs in Detail3:00
- 5.1 Capabilities & Applications of SpeechLM: Semantic-Related Tasks - 1 E2E ASR9:35
Capabilities and Applications of SpeechLMs: Semantic-Related Tasks - Introduction
End-to-End Automatic Speech Recognition (ASR)
- 5.1 Capabilities : Semantic-Related - 2 Zero-Shot TTS, Speech Translation (ST13:34
Zero-Shot Text-to-Speech (TTS)
Speech Translation (ST)
Challenges & Considerations
Summary & Key Takeaways
- Codes 5.1 - Whisper ASR Word-Level Timestamp + Zero-Shot Voice Cloning YourTTS7:14
- Quiz 5.1 Capabilities and Applications of SpeechLMs: Semantic-Related Tasks
- 5.2 Capabilities & Applications SpeechLM: Speaker-Related Tasks - 1 Introduction12:25
Capabilities and Applications of SpeechLMs: Speaker-Related Tasks- Introduction
- 5.2 Capabilities - 2 Speaker Identification & Verification, Personalized Speech9:06
Speaker Identification and Verification
Personalized Speech Synthesis
Challenges & Considerations
Summary & Key Takeaways
- Codes 5.2 - Speaker Verification with ECAPA-TDNN Embeddings + Voice Cloning8:17
Learn speaker verification and voice cloning using ECAPA-TDNN embeddings, cosine similarity thresholds, and three-sample audio workflows with Librispeech data, including personalized voice generation.
- Quiz 5.2 Capabilities and Applications of SpeechLMs: Speaker-Related Tasks
- 5.3 Paralinguistic Applications SpeechLMs -1 Speech Emotion Recognition (SER)15:46
Capabilities and Applications of SpeechLMs: Paralinguistic Applications – Introduction
Paralinguistics Deep Dive
Speech Emotion Recognition (SER)
Emotional Speech Generation
- 5.3 Paralinguistic - 2 Emotional Speech Generation, EMOVA,Prosody Control, pGSLM11:53
EMOVA - The Emotion Control Hub
Prosody Control
TTS Control Techniques
pGSLM: Precision Prosody Control
Challenges and Considerations
Summary & Key Takeaways
- Codes 5.3 - Speech Emotion Recognition + Prosody-Controlled Speech Synthesis11:44
Explore speech emotion recognition with the wave two vec two model to classify emotions from audio and map them on valence and arousal. Learn prosody-controlled speech synthesis using pitch, speed, and emphasis.
- Quiz 5.3 Paralinguistic Applications of SpeechLMs
- 5.4 Advanced Voice Interaction w SpeechLMs - 1 The Latency Challenge, RT Voice15:52
Capabilities and Applications of SpeechLMs: Advanced Voice Interaction – Introduction
The Latency Challenge
Real-Time Voice Interaction
- 5.4 Adv. - 2 LSLM Model, Advance Turn Detection,Interactive Period Recognition12:53
LSLM Model & Challenges
Advanced Turn Detection
Interactive Period Recognition
Challenges and Considerations
Summary & Key Takeaways
- Codes 5.4 -RT ASR w/ VAD & Interp. Handling + Turn-Taking Predn. in Conversation8:05
Explore building real-time voice interaction systems using VAD and interruption handling with whisper-based ASR, buffering, and exit commands, plus a CNN-based turn-taking predictor for conversational AI.
- Quiz 5.4 5.4 Advanced Voice Interaction with SpeechLMs
- Introduction to Module 6 - Evaluation Metrics and Benchmarking of SpeechLMs2:52
Evaluate speech language models with metrics like WER, SS, and F1 score, explore benchmarking datasets, and compare traditional methods to end-to-end speech language model approaches.
- 6.1 Evaluation metrics for SpeechLMs - 1 Introduction, Word Error Rate (WER)20:04
Evaluating Speech Language Models – Introduction
1. Word Error Rate (WER)
- 6.1 Eval.- 2 2. Speaker Similarity(SS),3. Speech Naturalness(MoS), Benchmarking13:32
2. Speaker Similarity (SS)
3. Speech Naturalness
Benchmarking SpeechLMs
Challenges & Considerations
Summary & Key Takeaways
- Codes 6.1 - Comprehensive ASR Evaluation + TTS Quality Evaluation Framework11:29
Evaluate speech language models with a comprehensive ASR framework using distill whisper, wer and cer, analyze error types, and assess TTS quality with MOS-like metrics and speaker similarity.
- Quiz 6.1 Common Evaluation metrics for SpeechLMs
- 6.2 Evaluating & Benchmarking SpeechLMs - 1 1.ASR 2.TTS13:44
Evaluating and Benchmarking SpeechLMs – Introduction
Evaluation of Different Capabilities and Corresponding Metrics:
1.Automatic Speech Recognition (ASR)
2.Text-to-Speech (TTS)
- 6.2 Eval - 2 3. Voice Conversion (VC), 4.Paralinguistic Apps,5. Intent Recognit12:56
3. Voice Conversion
4.Paralinguistic Applications
5.Speech Understanding (Intent Recognition and Sentiment Analysis)
- 6.2 Eval - 3 6. Sentiment Analysis 7. Speech-to-Speech Translation, Benchmarking11:57
6.Speech-to-Speech Translation (S2ST)
Benchmarking Across Capabilities
The Benchmarking Framework
Challenges & Considerations
Summary & Key Takeaways
- Codes 6.2 - ASR w/ Emotin Recognition + TTS/VC Eval w/ Acoustic Feature Analys7:08
- Quiz 6.2 Evaluating and Benchmarking Speech Language Models (SpeechLMs)
- 6.3 Benchmarking Datasets fSpeechLMs - 1 The Importance of Benchmarking Dataset10:54
Explore benchmarking datasets for speech language models, learn standardization, task-specific metrics, and how datasets like Librispeech and Common Voice enable fair progress, multilingual coverage, and robust evaluation.
- 6.3 Bench. - 2 Commonly Used Benchmarking Datasets by Capability,Using Datasets11:06
Explore task-based data sets for speech language models, compare ASR datasets such as Librispeech, Common Voice, and Switchboard, and learn benchmarking workflows, metrics, and leaderboards emphasizing data quality and diversity.
- Codes 6.3 - Custom ASR + Secure TTS Benchmarkng Framewk w/ SpeechT5 and Pyannote5:17
- Quiz 6.3 Benchmarking Datasets for Speech Language Models (SpeechLMs)
- 6.4 Comparing SpeechLMs w/ Traditional ASR, TTS, & Translation System - 1 Intro18:14
Compare traditional cascaded ASR, translation, and TTS with unified speech language models, evaluate architecture, inputs and outputs, establish benchmarks, and compare latency, error rates, and energy trade-offs.
- 6.4 Comparing - 2 Unified SpeechLM , Integrated Capab.Benchmarking Methodologies15:57
Speech language models routinely outperform traditional ASR, TTS, and translation on task-specific evaluations, while raising energy and deployment trade-offs. A hybrid approach, benchmarks, and ethical considerations guide their use.
- Codes 6.4 Comparing SpeechLM vs Traditional ASR System + Emotion Preservation5:52
- Quiz 6.4 Comparing SpeechLMs w/ Traditional ASR, TTS, and Translation System
- Introduction to Module 7 - Challenges and Future Directions in SpeechLM Research3:38
Explore challenges and future directions in speech language model research. Examine component choices, end to end training, scaling laws, paralinguistic modeling, low resource languages, real time full duplex interactions, and safety and privacy considerations.
- 7.1 Understanding Component Choices in SpeechLMs - 1 Key Components SpeechLMs11:26
Understanding Component Choices in Speech Language Models – Introduction
Key Components of a SpeechLM (Conceptual View)
- 7.1 Understanding Choices - 2 The Interplay and Importance of Component Choices10:47
The Interplay and Importance of Component Choices
The Need for Comprehensive Comparisons
Challenges & Considerations
Summary & Key Takeaways
- Codes 7.1 - Comparing Speech Feature Extractor + Vocoder Comparison Framework7:08
- Quiz 7.1 Understanding Component Choices in Speech Language Models
- 7.2 End-to-End Training of SpeechLMs - 1 Understanding End-to-End Training9:12
End-to-End Training of Speech Language Models –Introduction
Understanding End-to-End Training
Components of SpeechLM and End-to-End Training
- 7.2 End-to - 2 Core Components -The SpeechLM Engine, E2E The Performance Edge10:54
Potential Benefits of End-to-End Training
Key Considerations
Challenges of End-to-End Training
Summary & Key Takeaways
- Codes 7.2 - End-to-End Speech Recognition Training + Lite Tacotron TTS Training9:05
Train an end-to-end speech recognition model on a Librispeech subset using CTC, with a convolutional front end and an LSTM encoder, plus Tacotron lite for mel spectrograms, saving .pt weights.
- Quiz 7.2 End-to-End Training of SpeechLM Components
- 7.3 Scaling SpeechLMs to Larger Sizes and Datasets - 1 Triple Scaling Effect10:43
Scaling Speech Language Models to Larger Sizes and Datasets – Introduction
The Concept of Scaling in SpeechLMs
- 7.3 Scaling - 2 Data Scaling Mechanics, The SpeechLM Scaling Triad, Summary10:15
Impact of Model Size on SpeechLM Performance
Impact of Training Data Scale on SpeechLM Performance
Optimal Scaling and Trade-offs
Key Considerations
Summary & Key Takeaways
- Codes 7.3 - Scalable Speech Recog Training + Dataset caching, dynamic Bucketing11:16
Scale speech recognition under limited compute using chunked transformer with local and global attention, trained via ctc on Librispeech, and boost efficiency with data caching and dynamic bucketing.
- Quiz 7.3 Scaling Speech Language Models to Larger Sizes and Datasets
- 7.4 Improving Modeling Paralinguistic Information in SpeechLMs - 1 Challenges13:52
Improving Modeling of Paralinguistic Information in Speech Language Models – Introduction
What is Paralinguistic Information?
Why is Modeling Paralinguistic Information Important for SpeechLMs?
Challenges in Modeling Paralinguistic Information
- 7.4 Improving - 2 Advanced Paralinguistic Techniques, Multimodal ParalinGPT11:38
Ongoing Research and Techniques
Multimodal Approaches with Large Language Models
Leveraging Frozen Large Language Models
Addressing Subjectivity in Data Labeling
Summary & Key Takeaways
- Codes 7.2 - Emotion Recog w/ HuBERT Model + Prosody-Control Synthesis FastPitch6:26
- Quiz 7.4 Improving Modeling of Paralinguistic Information in SpeechLMs
- 7.5 Handling Low-Resource Languages - 1 Transfer Learning ,Self-Supervised18:55
Handling Low-Resource Languages for Speech Language Models – Introduction
What are Low-Resource Languages (LRLs)?
Core Challenge: Data Scarcity
Specific Challenges for LRL SpeechLMs
Strategies for Low-Resource Languages (LRLs) :
1 - Transfer Learning (Cross-Lingual Learning)
2 - Self-Supervised Learning (SSL)
3 - Data Augmentation
- 7.5 Handling - 2 Semi-Supervised Learning, Leveraging Related Languages14:28
4 - Semi-Supervised Learning
5 - Leveraging Related Languages
6 - Multitask Learning
7 - Active Learning
8 - Community Engagement & Crowdsourcing
Additional Challenges for LRL SpeechLMs
Practical Implementation Considerations
Summary & Key Takeaways
- Codes 7.5 - Fine-Tuning XLS-R for ASR + Emotion Classification with SpecAugment9:19
Apply transfer learning to fine-tune the XLS-R model for ASR and emotion classification using synthetic data, freezing base layers and training only the classification head with CTC loss and specaugment.
- Quiz 7.5 Handling Low-Resource Languages for Speech Language Models
- 7.6 Developing Real-Time and Duplex SpeechLMs - 1 Real-Time Duplex Architecture22:13
Developing Real-Time and Duplex SpeechLMs – Introduction
Real-Time vs. Duplex
The Foundations of Real-Time Speech Processing
Streaming vs. Batch Processing
- 7.6 Developing - 2 Streaming Architectures & Model Optimization, VAD, Barge-In17:40
The Real-Time SpeechLM Pipeline
Architectural Approaches: End-to-End vs. Modular
Core Challenges in Real-Time and Duplex Operation
Key Strategies for Achieving Real-Time Performance
Mastering Conversational Dynamics: Duplex, Turn-Taking, and Interruption
Additional Challenges for LRL SpeechLMs
Practical Implementation Considerations
Summary & Key Takeaways
- Codes 7.6 Streaming ASR w/ Causal Transformer Low-Latency + VAD for Barge-In Sys7:45
Showcases a streaming ASR using a causal transformer with 200 ms chunked attention for low latency, plus VAD to detect barge-in in real-time speech applications.
- Quiz 7.6 Developing Real-Time and Duplex SpeechLMs
- 7.7 Addressing Safety & Ethical Concerns in SpeechLMs - 1 SpeechLM Safety Risks10:51
Ethical Imperatives for SpeechLMs
SpeechLM Safety Risks - Part 1
- 7.7 Address - 2 Data & Model Layer, Security & Privacy Layer, Ensuring Accountab12:55
SpeechLM Safety Risks - Part 2
Mitigation Strategies - Data & Model Layer
Security & Privacy Layer
Ensuring Accountability
Summary & Key Takeaways
- Codes 7.7 Bias Eval ASR Accent Fairness + TTS Moderation with Toxicity Filterng8:58
- Quiz 7.7 Addressing Safety and Ethical Concerns in SpeechLMs
Requirements
- No prior speech AI experience required – beginner-friendly with hands-on guidance!
- A computer with Python 3.7+, TensorFlow/PyTorch, and audio libraries (e.g., Librosa).
- Basic Python programming (familiarity with loops, functions, and libraries like NumPy).
Description
Transform your understanding of voice AI with this comprehensive course on Speech Language Models (SLMs) - the revolutionary technology that's replacing traditional speech processing pipelines with powerful end-to-end solutions.
What You'll Master:
Speech Language Models represent the next frontier in AI, moving beyond the limitations of traditional ASR→LLM→TTS pipelines. This course takes you from fundamental concepts to advanced applications, covering everything from speech tokenization and transformer architectures to emotion AI and real-time voice interactions.
Why This Course Matters:
Traditional speech processing suffers from information loss, high latency, and error accumulation across multiple stages. SLMs solve these problems by processing speech directly, capturing not just words but emotions, speaker identity, and paralinguistic cues that make human communication rich and nuanced.
What Makes This Course Unique:
Hands-on Learning: Work with state-of-the-art models like YourTTS, Whisper, and HuBERT
Complete Pipeline Coverage: From raw audio to deployed applications
Real-world Applications: Build ASR systems, voice cloning, emotion recognition, and interactive voice agents
Latest Research: Covers cutting-edge developments in the rapidly evolving SLM field
Practical Implementation: Learn training methodologies, evaluation metrics, and deployment strategies
Key Technologies You'll Work With:
Speech tokenizers (EnCodec, HuBERT, Wav2Vec 2.0)
Transformer architectures adapted for speech (Whisper , Conformer models etc)
Vocoder technologies (Tacotron, Hi-Fi GAN, MelGAN etc)
Multi-modal training approaches (CTC, UCTC etc
Parameter-efficient fine-tuning (LoRA)
Perfect For:
AI/ML engineers wanting to specialize in speech technology
Students or Career Changers
Researchers exploring next-generation voice AI
Developers building voice-first applications
Anyone curious about how modern voice assistants really work
Course Outcome:
By completion, you'll have the skills to design, train, and deploy Speech Language Models for diverse applications - from basic speech recognition to sophisticated emotion-aware voice agents. You'll understand both the theoretical foundations and practical implementation details needed to contribute to this exciting field.
Join the voice AI revolution and master the technology that's reshaping human-computer interaction!
Who this course is for:
- This course is for aspiring AI developers, data scientists, and tech enthusiasts eager to pioneer the future of voice AI with Speech Language Models.
- Perfect for beginners with basic Python and ML skills, as well as intermediate learners aiming to build advanced applications like real-time speech recognition, emotion-aware voice assistants, and speech translation.
- Unlock the power of end-to-end speech processing for cutting-edge careers in AI!