Ensemble BPE Speech Recognition Combining Phone and Character Models
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Solution Overview
Problem
Modern end-to-end speech recognition systems face challenges in improving accuracy and optimizing automatic speech recognition using byte pair encoding (BPE) techniques, as they often rely on conventional modeling units like phones and context-dependent variants, which are not as effective as character and character-based sub-words.
Innovation Solution
An ensemble byte pair encoder (BPE) system is introduced, combining a phone BPE system and a character BPE system, utilizing a multi-level language model with sub-word and word LMs, and a one-pass beam search decoder algorithm to efficiently convert spoken utterances into textual representations, leveraging the complementarity between phone and character BPE systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional phone-based modeling units are used in speech recognition systems, then the system structure is simple and easy to implement, but the speech recognition accuracy is insufficient
Solution Approach 1:
The patent combines phone-based BPE and character-based BPE into a single ensemble system that processes speech inputs through both modeling units simultaneously. The system merges the outputs of two separate BPE processes, allowing it to leverage the phonetic information from phone-based units while capturing subword patterns from character-based units, thereby improving recognition accuracy without requiring a complete system redesign
Solution Approach 2:
The system uses a composite approach by integrating two different BPE methodologies (phone-based and character-based) into a unified ensemble framework. This composite structure allows the system to benefit from the strengths of both approaches: phone-based BPE excels at capturing phonetic patterns while character-based BPE captures morphological and orthographic patterns, creating a more robust speech recognition system
2Reliability
If a single BPE system (either phone-based or character-based) is used, then the system complexity is low, but the speech recognition accuracy and robustness are limited
Solution Approach 1:
The patent implements an ensemble system that merges phone-based BPE and character-based BPE outputs through a unified decoding process. The system combines scores from both BPE models and uses beam search to select the most probable transcription, thereby improving reliability through diversity in modeling approaches while maintaining a coherent system architecture
Solution Approach 2:
The system incorporates feedback mechanisms by using beam search decoding that evaluates and compares hypotheses from both phone-based and character-based BPE systems. The decoding process iteratively refines transcriptions by incorporating score information from both BPE models, allowing the system to leverage complementary strengths and improve overall robustness through iterative optimization
3Measurement precision
If phone-based BPE and character-based BPE systems are used separately, then each system is simple to implement, but the overall speech recognition performance is suboptimal
Solution Approach 1:
The patent merges the decoding processes of phone-based and character-based BPE systems into a unified one-pass beam search algorithm. The system combines scores from both BPE models at each decoding step and selects the best hypothesis, achieving improved word error rate performance while avoiding the need for separate independent decoding processes
Solution Approach 2:
The ensemble decoding system serves multiple functions simultaneously: it processes both phone-based and character-based BPE inputs, performs unified score combination, and generates a single optimal transcription output. This multi-functional approach allows the system to handle diverse input types while maintaining a streamlined decoding process that improves overall recognition performance
Data Source
AI summary
System and methods for identifying a text word from a spoken utterance are provided. An ensemble BPE system that includes a phone BPE system and a character BPE system receives a spoken utterance. Both BPE systems include a multi-level language model (LM) and an acoustic model. The phone BPE system identifies first words from the spoken utterance and determine a first score for each first word. The first words are converted into character sequences. The character BPE model converts the character sequences into second words and determines a second score for each second word. For each word from the first words that matches a word in the second words the first and second scores are combined. The text word is the word with a highest score.