This paper is concerned with automatic continuous speech recognition using trainable systems. The aim of this work is to build acoustic models for spoken Swedish. This is done employing hidden Markov models and using the SpeechDat database to train their parameters. Acoustic modeling has been worked out at a phonetic level, allowing general speech recognition applications, even though a simplified task (digits and natural number recognition) has been considered for model evaluation. Different kinds of phone models have been tested, including context independent models and two variations of context dependent models. Furthermore many experiments have been done with bigram language models to tune some of the system parameters. System performance over various speaker subsets with different sex, age and dialect has also been examined. Results are compared to previous similar studies showing a remarkable improvement.

翻译：本文关注基于可训练系统的连续语音自动识别。研究目标是为瑞典语口语构建声学模型，采用隐马尔可夫模型并通过SpeechDat数据库训练其参数。声学建模在音素层面展开，可支持通用语音识别应用，尽管模型评估阶段采用了简化任务（数字与自然数识别）。研究测试了不同类型的音素模型，包括上下文无关模型及两种上下文相关变体。此外，基于二元语言模型开展了大量实验以优化系统参数。论文还考察了系统在不同性别、年龄及方言说话者子集上的表现。与既往同类研究相比，实验结果表明系统性能获得显著提升。