Multi-Blank Transducers for Faster and More Accurate Speech Recognition

Основные понятия

The proposed multi-blank RNN-T method introduces additional blank symbols that consume multiple input frames, enabling faster inference while improving speech recognition accuracy.

Аннотация

The paper proposes a modification to the standard RNN-Transducer (RNN-T) model for automatic speech recognition (ASR). In standard RNN-T, the emission of a blank symbol consumes exactly one input frame. The authors introduce additional "big blank" symbols that consume two or more input frames when emitted, referred to as multi-blank RNN-T.
To prioritize the emission of big blanks, the authors propose a novel logit under-normalization method during training. Experiments on multiple languages and datasets show that multi-blank RNN-T methods can bring relative speedups of over 90% and 139% for English Librispeech and German Multilingual Librispeech datasets, respectively, while also improving ASR accuracy consistently.
The key highlights are:

Multi-blank RNN-T models introduce additional blank symbols that consume multiple input frames, enabling faster inference.
A logit under-normalization method is proposed to prioritize the emission of big blanks during training.
Experiments show significant speedups (up to 92.9% for English, 139.6% for German) and consistent accuracy improvements.
The method is straightforward to implement as an extension to standard RNN-T models.

Статистика

On Librispeech test-other, the baseline RNN-T model takes 243 seconds for inference.
With multi-blank RNN-T models, the inference time is reduced to 126 seconds, a 92.9% speedup.
On German Multilingual Librispeech, the baseline RNN-T takes 544 seconds, while the multi-blank model takes 227 seconds, a 139.6% speedup.

Цитаты

"With experiments on multiple languages and datasets, we show that multi-blank RNN-T methods could bring relative speedups of over +90%/+139% to model inference for English Librispeech and German Multilingual Librispeech datasets, respectively."
"The multi-blank RNN-T method also improves ASR accuracy consistently."

Ключевые выводы из

Multi-blank Transducers for Speech Recognition

by Hainan Xu,Fe... в arxiv.org 04-15-2024

https://arxiv.org/pdf/2211.03541.pdf

Дополнительные вопросы

How could the multi-blank RNN-T method be extended to support beam search decoding for improved accuracy

To extend the multi-blank RNN-T method to support beam search decoding for improved accuracy, we can modify the decoding algorithm to incorporate beam search. During beam search, instead of selecting the token with the highest probability at each step, we maintain a beam of the top-k candidates. This allows for exploring multiple paths simultaneously and selecting the most likely sequence based on a scoring mechanism that considers the probabilities of the emitted tokens.
In the context of multi-blank RNN-T, beam search can be adapted to handle the emission of big blanks with durations greater than one frame. When a big blank is emitted, the decoding process should advance the input by the corresponding duration, ensuring that the beam search algorithm considers the impact of big blanks on the overall sequence. By incorporating the duration of big blanks into the beam search decoding process, the model can make more informed decisions and potentially improve accuracy by capturing longer-range dependencies in the input sequence.

What are the potential challenges in deploying the multi-blank RNN-T model in a streaming speech recognition system, and how could they be addressed

Deploying the multi-blank RNN-T model in a streaming speech recognition system poses several challenges that need to be addressed for successful implementation. One major challenge is the real-time nature of streaming systems, where the model must process input data incrementally and provide timely responses.
One potential challenge is the variable duration of big blanks in the multi-blank RNN-T model. In a streaming scenario, where input frames arrive continuously, the model needs to handle the emission of big blanks that span multiple frames efficiently. This requires careful synchronization between the model's decoding process and the incoming audio stream to ensure accurate alignment and processing of big blanks.
Another challenge is maintaining low latency while processing big blanks, which consume multiple input frames. To address this, optimization techniques such as efficient buffering, parallel processing, and hardware acceleration can be employed to speed up the inference process and reduce latency. Additionally, adaptive strategies for adjusting the duration of big blanks based on the input stream's characteristics can help optimize performance in a streaming environment.
Ensuring robustness to variations in input quality and handling interruptions or changes in the audio stream are also critical considerations for deploying the multi-blank RNN-T model in a streaming speech recognition system. Implementing mechanisms for error handling, buffering, and dynamic adjustment of model parameters can help mitigate these challenges and enhance the system's overall reliability and performance.

Could the multi-blank concept be applied to other sequence-to-sequence models beyond speech recognition, such as machine translation or text generation

The concept of multi-blank modeling in RNN-T can be extended to other sequence-to-sequence tasks beyond speech recognition, such as machine translation or text generation, to improve model efficiency and performance. By introducing big blanks with explicitly defined durations, models can capture longer-range dependencies and optimize the emission of tokens in the output sequence.
In machine translation, applying the multi-blank concept could help address issues related to long-distance dependencies between source and target languages. By allowing the model to emit big blanks that span multiple tokens, it can better capture complex linguistic structures and improve translation accuracy, especially for languages with different word orders or syntactic patterns.
Similarly, in text generation tasks, incorporating multi-blank modeling can enhance the generation of coherent and contextually relevant sequences. By introducing big blanks with varying durations, the model can learn to generate more structured and coherent text by considering dependencies across multiple tokens and improving the flow and coherence of the generated output.
Overall, extending the multi-blank concept to other sequence-to-sequence models opens up opportunities to enhance the performance and efficiency of various natural language processing tasks by enabling models to capture longer-range dependencies and improve the overall quality of generated sequences.