Revolutionizing Speech Recognition: Introducing Acoustic Model Fusion for Enhanced Accuracy

Understanding Acoustic Model Fusion in End-to-End ASR Systems

Developments in Automatic Speech Recognition (ASR) have enhanced system accuracy and efficiency. Integrating an external Acoustic Model (AM) into End-to-End (E2E) ASR systems through Acoustic Model Fusion (AMF) by Apple resolves persistent domain mismatch issues in speech recognition. This amalgamation aims to improve speech recognition by exploiting external acoustic models alongside the capabilities of E2E systems.

Limitations of E2E ASR Systems

While E2E ASR systems offer streamlined architecture and efficiency, they face challenges with rare or complex words underrepresented in training data. Introducing external Acoustic Model Fusion (AMF) refines the system’s affinity with diverse real-world applications, specifically enhancing recognition of named entities and rare words.

Testing and Results

The efficacy of AMF was tested in various scenarios, with results indicating a significant reduction in Word Error Rates (WER) – up to 14.3% across different test sets. This signals the potential of AMF in enhancing ASR accuracy and recognizing named entities and rare words. Furthermore, AMF demonstrates superiority over traditional language model integration techniques.

The success of AMF in addressing domain mismatches and enhancing word recognition points towards more accurate, efficient, and adaptable speech recognition systems, paves the way for future advancements, and enriches human-computer interaction through speech.

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