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On that subject I recommend you to read a very good paper by James Baker and others who were actually responsible for introduction of HMM in speech: A Historical Perspective of Speech Recognition http://cacm.acm.org/magazines/2014/1/170863-a-historical-perspective-of-speech-recognition/abstract Using Markov models to represent language knowledge was ...


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Have speech models advanced past the need for any personalization of the training data? There were two aspects which improved accuracy significantly: Deep learning and neural networks greatly improved the accuracy. Amount of training data that major companies use has grown over years by order of magnitude. Companies collected so much data that effect of ...


3

Simple neural network as a structure doesn't have invariance across time scale deformation that's why it is impractical to apply it to recognize time series. To recognize time series usually a generic communication model is used (HMM). NN could be used together with HMM to classify individual frames of speech. In such HMM-ANN configuration audio is split on ...


3

I want to build a Automatic Speech Recognition (ASR) engine for myself, but I've no idea from where to start. Start with trying existing open source speech recognition system, learn how they work, play with them. Check http://cmusphinx.sourceforge.net. I've read that most ASR's are build upon Hidden Markov Models, but also I've read that HMM is limited ...


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Hidden Markov Models were used to model phoneme units in words for speech recognition starting in the late 1980s. an early paper cited is [9] in the following. Levinson, Ljolje, Miller, "Large vocabulary speech recognition using a hidden Markov model for acoustic/ phonetic classification" in Proc. IEEE Intl. Conf. Acoust., Speech, SIgnal Processing (New ...


3

The fact that humans can understand audio books shows that it is computationally possible. This does not make the problem easy for an AI. It also does not mean that you can write a formalism of the language in audio form for parsing. Anything involving audio / recordings is going to involve lots of fuzzy data. This is not generally what someone would call ...


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Papers on algorithms: A RECURSIVE ALGORITHM FOR THE FORCED ALIGNMENT OF VERY LONG AUDIO SEGMENTS Pedro J. Moreno, Chris Joerg, Jean-Manuel Van Thong and Oren Glickman Automatic Alignment and Error Correction of Human Generated Transcripts for Long Speech Recordings Timothy J. Hazen SailAlign: Robust long speech-text alignment Authors Katsamanis, Athanasios ...


1

Software There are many many speech recognition toolkits and software packages, which should be able to do what you want. See, e.g., https://en.wikipedia.org/wiki/List_of_speech_recognition_software for a partial list. For instance, if you're looking for open source software, you could try Sphinx, Simon, or any number of other software packages. They can ...


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Normally in speech-to-text we don't already have a perfect segmentation into words, which is why we often use the HMM to match the entire speech sentence. Also, this way the HMM can take into account probabilistic information on the distribution of word bigrams (and even trigrams). If you already have a segmentation and you are sure it is correct, then you ...


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