针对日语口语评估，构建带声调标记的音素识别器
Building Tailored Speech Recognizers for Japanese Speaking Assessment
- 多任务学习融合文本和音高预测，利用仅有文字标注的数据
- 融合音素字符串与词元序列估计结果，误差率从12.3%降至7.1%
- 适合需要精确声调识别的日语语音评估系统开发者
本文提出构建面向日语口语评估的语音识别器的方法。该识别器输出带声调标记的音素标签。尽管日语资源丰富，但用于训练生成准确音素转录（含声调标记）的数据仍十分有限。为此，我们提出两种缓解数据稀疏性的方法：一是采用多任务训练，引入辅助损失函数以估计输入信号的正字法文本和音高模式，从而利用仅具正字法标注的语句进行训练；二是融合两个估计器——一个基于音素字母串，另一个基于文本词元序列，并基于有限状态转换器框架开发算法进行结合。实验表明，多任务学习与融合策略能有效提升音素识别准确性。相比通用多语言识别器，本方法更具优势。在CSJ核心测试集上，平均音拍标签错误率由12.3%降低至7.1%。
原文摘要 · Abstract (English)
This paper presents methods for building speech recognizers tailored for Japanese speaking assessment tasks. Specifically, we build a speech recognizer that outputs phonemic labels with accent markers. Although Japanese is resource-rich, there is only a small amount of data for training models to produce accurate phonemic transcriptions that include accent marks. We propose two methods to mitigate data sparsity. First, a multitask training scheme introduces auxiliary loss functions to estimate orthographic text labels and pitch patterns of the input signal, so that utterances with only orthographic annotations can be leveraged in training. The second fuses two estimators, one over phonetic alphabet strings, and the other over text token sequences. To combine these estimates we develop an algorithm based on the finite-state transducer framework. Our results indicate that the use of multitask learning and fusion is effective for building an accurate phonemic recognizer. We show that this approach is advantageous compared to the use of generic multilingual recognizers. The relative advantages of the proposed methods were also compared. Our proposed methods reduced the average of mora-label error rates from 12.3% to 7.1% over the CSJ core evaluation sets.
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