Multi-Task Speech Transformer for Decoder-Free ASR Deployment
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Solution Overview
Problem
Conventional speech recognition systems face challenges in working reliably across diverse environments without requiring supervised fine-tuning of decoders for every deployment distribution, due to the lack of performant decoders for unsupervised pre-trained audio encoders and the complexity of handling tasks like transcription, translation, and voice activity detection separately.
Innovation Solution
A multi-task automatic speech recognition system using a transformer model with an encoder and decoder, trained on large labeled datasets for multi-language and multi-task operations, incorporating special tokens for task specification and timestamp prediction, enabling robust performance without fine-tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate components are used for voice activity detection, speaker diarization, and inverse text normalization, then each component can be optimized independently, but the overall system complexity increases
Solution Approach 1:
The patent combines multiple separate speech processing components (voice activity detection, speaker diarization, inverse text normalization) into a unified transformer model that processes speech inputs and generates comprehensive outputs including transcripts, speaker labels, and timestamps in a single integrated system
Solution Approach 2:
The transformer model is designed to perform multiple functions simultaneously - it can detect voice activity, identify speakers, transcribe speech, and provide time alignment all through a single multi-task model that handles diverse speech processing tasks within one unified architecture
2Ease of manufacture
If unsupervised pre-training techniques are used to train audio encoders from unlabeled speech, then training data requirements are reduced, but the lack of performant decoders limits the usefulness and robustness
Solution Approach 1:
The patent combines encoder and decoder training into a unified joint training process where both components are trained together on the same dataset, allowing the decoder to learn effective mappings from encoder representations to target outputs while the encoder learns robust feature extraction, achieving better overall system performance than separate training approaches
3Reliability
If supervised fine-tuning of decoders is performed for every deployment distribution, then decoder performance for specific tasks is improved, but the deployment process becomes complex and time-consuming
Solution Approach 1:
The transformer model is designed as a universal multi-task system that can handle multiple speech processing tasks (transcription, translation, voice activity detection, time alignment, language identification) through a single model architecture, eliminating the need for separate fine-tuned decoders for each task and enabling immediate deployment across diverse applications
Data Source
AI summary
Disclosed herein are methods, systems, and computer-readable media for generating an output transcript from an input audio segment using a multi-task transformer model. In some embodiments, the transformer model can be trained to transcribe or translate audio data in multiple languages using labeled audio data. The labeled audio data can include first audio segments associated with first same-language transcripts of the first audio segments and second audio segments associated with second different-language transcripts of the second audio segments. In some embodiments, a vocabulary of the model can include special purpose and time stamp tokens. The special purpose tokens can specify tasks for the model to perform.