通过两阶段转写提升多语言语音识别准确率
A two-stage transliteration approach to improve performance of a multilingual ASR
- 将多种语言的字符映射到通用的天城文脚本，构建语言无关模型
- 在尼泊尔语和泰卢固语上实现WER降低20%、CER降低24%
- 适合处理多语言混合场景下的语音识别任务
端到端自动语音识别（ASR）系统正快速成为主流。尽管已有多种技术提升其多语言能力，但不同语言书写系统差异导致声学相似单元解码时难以对应目标语言的正确字形，限制了模型在多语言混用场景下的可扩展性与适应性。本文提出一种两阶段转写方法，将多语言字形数据投影至更通用的天城文脚本，构建语言无关的端到端模型。该方法避免了声学模型重新训练以覆盖更大空间，且易于扩展至多语言。实验基于尼泊尔语和泰卢固语的多语言语音识别系统，在转写后的天城文空间中，相比传统语言依赖方法，词错误率（WER）降低20%，字符错误率（CER）降低24%。
原文摘要 · Abstract (English)
End-to-end Automatic Speech Recognition (ASR) systems are rapidly claiming to become state-of-art over other modeling methods. Several techniques have been introduced to improve their ability to handle multiple languages. However, due to variation in writing scripts for different languages, while decoding acoustically similar units, they do not always map to an appropriate grapheme in the target language. This restricts the scalability and adaptability of the model while dealing with multiple languages in code-mixing scenarios. This paper presents an approach to build a language-agnostic end-to-end model trained on a grapheme set obtained by projecting the multilingual grapheme data to the script of a more generic target language. This approach saves the acoustic model from retraining to span over a larger space and can easily be extended to multiple languages. A two-stage transliteration process realizes this approach and proves to minimize speech-class confusion. We performed experiments with an end-to-end multilingual speech recognition system for two Indic Languages, namely Nepali and Telugu. The original grapheme space of these languages is projected to the Devanagari script. We achieved a relative reduction of 20% in the Word Error Rate (WER) and 24% in the Character Error Rate (CER) in the transliterated space, over other language-dependent modeling methods.
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