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.

本研究旨在通过训练系统实现自动持续语音识别，并以瑞典口语为例，使用隐藏马尔可夫模型，利用 SpeechDat 数据库进行参数训练。在这项研究中，声学建模在语音识别应用方面具有一定的普适性，尽管对模型评估仅考虑了一个简化的任务（数字和自然数识别）。研究还测试了不同类型的音素模型，包括独立上下文模型和两种上下文相关模型的变体。同时，还进行了多个使用双字语言模型调整系统参数的实验。研究还检查了在不同说话者子集（性别、年龄和方言）上系统性能的情况，并将结果与先前类似研究进行了对比，显示出明显的改进。