Hidden Markov model (HMM) is the base of a set of successful techniques for acoustic modeling in speech recognition systems. The main reasons for this success are due to this model's analytic ability in the speech phenomenon and its accuracy in practical speech recognition systems.

Hidden Markov Models (HMMs) provide a simple and effective frame-work for modelling time-varying spectral vector sequences. As a con-sequence, almost all present day large vocabulary continuous speech recognition (LVCSR) systems are based on HMMs.

2020年5月4日 · HMM里需要一个叫做state的东西，如上文所述，它在我们的声学模型里，就是一个比Phoneme更小的东西，那它究竟有多小呢？ 答案就是，它可以任意小，只要我们的计算资源足够就可以。

Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabulary continuous speech recognition (LVCSR) systems are based on HMMs.

Speech recognition is the task of identifying a sequence of words uttered by a speaker, given the acoustic signal. We can view speech recognition as a problem in most-likely-sequence …

2024年8月28日 · Through this iterative process, the HMM learns the patterns of speech, building a model that can recognize phonemes and words from new, unseen audio data.

The speech recognition system implemented during this project trains one hidden Markov model for each word that it should be able to recognize. The models are trained with labeled training data, and the classification is performed by passing …

Topics MarkovModelsandHiddenMarkovModels HMMs appliedtospeechrecognition Training Decoding Analog Speech

It's a demo project for simple isolated speech word recognition. There are only 100 audio files with extention of .wav for training, and 10 audio files for testing.

In this paper, we propose a hidden Markov model (HMM)-based speech enhancement method using explicit gain modeling. Through the introduction of stochastic gain variables, energy variation in both speech and noise is explicitly modeled in a unified framework.