SGER: Incorporating Higher-Level Information into Dynamic Pronounciation Modeling for ASR

SGER：将高级信息纳入 ASR 动态发音建模

• 批准号: 9713346
• 负责人: Nelson Morgan
• 金额: $ 3.81万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-10-01 至 1998-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9713346&HistoricalAwards=false
• 关键词: SGER; Incorporating; Higher; Level; Information

In large-vocabulary spontaneous speech, the variability of the pronunciations of words is much higher than in read speech situations. At the 1996 Summer Workshop on Large Vocabulary Conversational Speech Recognition (WS96), a model for this variability to be used in Automatic Speech Recognition (ASR) systems was developed based on machine- derived descriptions of speech data. The continuation of this work in this grant focuses on studying the correlation of variation in pronunciations in continuous speech and higher-level information not usually brought to bear in an ASR pronunciation model. One important element in this model is the rate of speech, which has been shown to be a good predictor of word error rate on both read and spontaneous speech corpora. Investigations into the effects of resyllabification (movement of syllable boundaries when words are spoken in sequence) and word frequency on word pronunciations are also undertaken. The goal of this project is to improve the predictability of variation for speech recognition models, in particular for the reduction of recognition error for spontaneous and conversational speech. The techniques will be evaluated on the Switchboard corpus.

在大词汇量的自发言语中，单词发音的变异性要比在朗读的情况下高得多。 在1996年的大词汇量会话语音识别（WS96）夏季研讨会上， 在自动语音识别（ASR）系统中使用的这种可变性是基于语音数据的机器导出的描述而开发的。 这项工作的继续在这个补助金的重点是研究在连续语音和更高层次的信息，通常不承担在ASR发音模型中的发音变化的相关性。 这个模型中的一个重要元素是语音速率，它已被证明是一个很好的预测词错误率在两个阅读 和自发语音语料库。 调查的影响resyllabification（移动音节边界时，单词顺序发言）和单词的发音频率也进行。 该项目的目标是提高语音识别模型变化的可预测性，特别是减少自发和会话语音的识别错误。 这些技术将在Switchboard语料库上进行评估。