特定任务的语音识别模型自适应

IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01. Pub Date : 2001-12-09 DOI:10.1109/ASRU.2001.1034677

A. Sankar, Ashvin Kannan, B. Shahshahani, E. Jackson

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引用次数: 3

摘要

大多数已发表的适应研究主要集中在说话人的适应，以及对噪声信道和背景环境的适应。我们研究声学、语法，并结合声学和语法适应来创建特定任务的识别模型。使用自然语言报价和交易应用程序的数据给出了综合实验结果。结果表明，任务自适应在语音理解准确率和识别速度上都有显著提高。

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Task-specific adaptation of speech recognition models

Most published adaptation research focuses on speaker adaptation, and on adaptation for noisy channels and background environments. We study acoustic, grammar, and combined acoustic and grammar adaptation for creating task-specific recognition models. Comprehensive experimental results are presented using data from natural language quotes and a trading application. The results show that task adaptation gives substantial improvements in both utterance understanding accuracy, and recognition speed.

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IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01.

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