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Multilingual Speech Recognition with Self-Attention Structured Parameterization

Yun Zhu
Bhuvana Ramabhadran
Brian Farris
Hainan Xu
Han Lu
Pedro Jose Moreno Mengibar
Qian Zhang
Interspeech 2020, 21st Annual Conference of the International Speech Communication Association, ISCA
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Abstract

Multilingual automatic speech recognition systems can transcribe utterances from different languages. These systems are attractive from different perspectives: they can provide quality improvements, specially for lower resource languages, and simplify the training and deployment procedure. End-to-end speech recognition has further simplified multilingual modeling as one model, instead of several components of a classical system, have to be unified. In this paper, we investigate a streamable end-to-end multilingual system based on the Transformer Transducer. We propose several techniques for adapting the self-attention architecture based on the language id. We analyze the trade-offs of each method with regards to quality gains and number of additional parameters introduced. We conduct experiments in a real-world task consisting of five languages. Our experimental results demonstrate $\sim$10\% and $\sim$15\% relative gain over the baseline multilingual model.

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