ASR Model Distillation With Chunked Speech for Utterance Mismatch
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems face challenges with diverse input utterances, leading to deletion and insertion errors due to data mismatch between training and test datasets, particularly affecting long-form and short-form utterances.
Innovation Solution
A unified multi-form acoustic model is developed using knowledge distillation, combining full utterance training with chunked data to fine-tune a student network, incorporating short-term spectro-temporal knowledge through a teacher-student network framework.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If full utterance training data is used to train the ASR model, then the model can handle long-form utterances, but it produces deletion and insertion errors on short-form utterances due to data mismatch
Solution Approach 1:
The training data is segmented into two distinct types: full utterance data for training the teacher model on long-form speech, and short-form utterance data for training the student model. This segmentation allows each model to specialize in specific utterance lengths, preventing the data mismatch problem that occurs when a single model is trained on mixed data.
Solution Approach 2:
A knowledge distillation framework is introduced as an intermediary mechanism to transfer knowledge from the teacher model (trained on full utterances) to the student model (trained on short-form utterances). This intermediary process enables the student model to learn from the teacher's expertise while maintaining specialization in short-form utterances, thereby improving overall ASR accuracy across diverse utterance lengths.
2Manufacturing precision
If separate models are trained for long-form and short-form utterances, then ASR accuracy improves for each type, but device complexity increases
Solution Approach 1:
The knowledge distillation framework serves as a universal mechanism that can transfer knowledge between models of different sizes and architectures. The teacher-student framework is designed to be architecture-agnostic, allowing the student model to have a simpler structure while still achieving high accuracy through knowledge transfer, thus reducing overall system complexity.
Solution Approach 2:
The student model is initialized as a copy or simplified version of the teacher model's architecture. By copying the essential structure and then fine-tuning it on short-form utterances with knowledge distillation, the system achieves specialized performance without designing entirely separate complex architectures for each utterance type.
3Ease of manufacture
If the model is trained on mixed data without chunking, then training is simpler, but the model fails to capture short-term spectro-temporal patterns
Solution Approach 1:
The audio input is segmented into short-time frames or chunks, allowing the model to process and learn short-term spectro-temporal patterns within each frame. This segmentation enables the capture of local temporal dynamics that would be lost in full-utterance processing, improving pattern recognition accuracy.
Solution Approach 2:
The training approach adds a temporal dimension by processing utterances in short-time frames rather than as complete sequences. This dimensional change from global to local processing allows the model to capture spectro-temporal patterns at multiple time scales, improving recognition precision while maintaining training feasibility through the teacher-student framework.
Data Source
AI summary
Mechanisms are provided for training an Automatic Speech Recognition (ASR) model. An automatic speech recognition (ASR) computer model is trained based on full utterance training data, the ASR computer model having an audio encoder, text predictor, and a joint network which combines outputs of both the audio encoder and the text predictor. Fine-tuning training of the ASR computer model is performed by a knowledge distillation framework at least by: executing a chunking operation on full utterance data to generate a plurality of data chunks corresponding to full utterances in the full utterance data; and executing a knowledge distillation operation with two encoder embeddings. A first encoder embedding is obtained from the full utterance data and a second encoder embedding is obtained from the data chunks. Operational parameters of the trained ASR model are updated based on a loss determined from the first encoder embedding and second encoder embedding.