Packt

SpeechLM Architecture, Training, and Advanced Applications

Packt

SpeechLM Architecture, Training, and Advanced Applications

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

1 week to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Design modern SpeechLM architectures for ASR and TTS applications

  • Implement audio tokenization and vocoder pipelines for speech synthesis

  • Deploy scalable multi-agent systems with low-latency voice interactions

Details to know

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Recently updated!

October 2026

Assessments

5 assignments

Taught in English

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This course is part of the Speech Language Models for Modern Voice AI Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

This module explores the architecture and components of SpeechLMs, focusing on speech tokenization, language models, and vocoders. Learners will gain hands-on experience with coding examples and comparative analysis of different speech synthesis techniques. The module emphasizes both theoretical understanding and practical implementation of agent memory systems.

What's included

13 videos1 reading1 assignment

This module explores advanced training methodologies for SpeechLMs, including pre-training, instruction-tuning, post-alignment, and real-world deployment. It covers practical implementation of multi-stage pipelines and techniques like LoRA-based PEFT and RLHF. Learners will gain hands-on experience with datasets, decoding strategies, and model alignment strategies.

What's included

13 videos1 assignment

This module explores the advanced capabilities of SpeechLMs in multi-agent systems, covering semantic tasks like ASR and TTS, speaker-related applications such as identification and verification, and paralinguistic features like emotion recognition and prosody control. Learners will gain hands-on experience implementing these technologies and understanding their real-world applications in voice AI systems.

What's included

13 videos1 assignment

This module provides an in-depth exploration of evaluation metrics, benchmarking strategies, and practical coding implementations for assessing SpeechLMs. Learners will gain hands-on experience with ASR, TTS, and speech translation evaluation frameworks, as well as strategies for comparing SpeechLMs with traditional systems.

What's included

14 videos1 reading2 assignments

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Packt
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