Sequence-to-Sequence Speech Recognition with Attention Mechanisms
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Solution Overview
Problem
Current speech recognition systems, particularly sequence-to-sequence models, face challenges in achieving high accuracy and low latency for tasks like voice search, with existing models struggling to outperform state-of-the-art systems on large vocabulary continuous speech recognition tasks and experiencing performance degradation in streaming applications.
Innovation Solution
The implementation of structural improvements such as word piece models and multi-headed attention, along with optimization techniques like minimum word error rate training, scheduled sampling, synchronous training, and label smoothing, within attention-based encoder-decoder architectures, enables the development of more accurate and efficient speech recognition systems that can match the performance of state-of-the-art models while maintaining low latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional speech recognition systems are used, then system simplicity is maintained, but speech recognition accuracy is insufficient
Solution Approach 1:
The patent merges acoustic model, pronunciation model, and language model into a single integrated sequence-to-sequence neural network architecture. This integration allows the system to achieve state-of-the-art speech recognition accuracy by leveraging the complementary strengths of each model component while eliminating the need for separate processing stages and reducing overall system complexity.
Solution Approach 2:
The patent segments the speech recognition task into distinct processing stages within the neural network: encoder for acoustic feature extraction, attention mechanism for temporal alignment, and decoder for transcription generation. This segmentation allows each component to be optimized independently while working together to achieve high recognition accuracy.
2Loss of time
If streaming sequence-to-sequence models are used, then low latency is achieved, but performance degradation occurs compared to non-streaming models
Solution Approach 1:
The patent pre-trains the streaming neural network model using non-streaming training data and training methods first. This preliminary training establishes strong baseline performance and proper temporal alignment before adapting the model to streaming conditions, thereby minimizing performance degradation while achieving low-latency operation.
Solution Approach 2:
The patent copies the architecture and training approach from successful non-streaming sequence-to-sequence models (like LAS) and adapts them for streaming operation. By replicating the core attention-based architecture and training methodology, the system maintains high accuracy while enabling low-latency streaming recognition.
Data Source
AI summary
A method for performing speech recognition using sequence-to-sequence models includes receiving audio data for an utterance and providing features indicative of acoustic characteristics of the utterance as input to an encoder. The method also includes processing an output of the encoder using an attender to generate a context vector, generating speech recognition scores using the context vector and a decoder trained using a training process, and generating a transcription for the utterance using word elements selected based on the speech recognition scores. The transcription is provided as an output of the ASR system.