Sentence examples for models in speech from inspiring English sources

These characteristics of the language result in vocabulary explosion, large number of out-of-vocabulary (OOV) words and an increased complexity of n-gram language models in speech recognition when words are used as recognition units.

The sound effects were modeled with HMMs, and a higher-level model was used to connect individual sound effect models through a grammar network similar to language models in speech recognition.

Grammar is a formal specification of permissible structures for the language that is used as another important linguistic knowledge source besides the statistical language models in speech recognition systems.

In this paper, the central question that is raised is at the heart of the ongoing debate with respect to the interaction versus autonomous processing models in speech perception research: How do auditory cues, phonological neighborhood, and word frequency contribute to phoneme identification errors in word recognition tasks?

This chapter focuses on problems posed by non-native speech input as well as accent and dialect variation with respect to acoustic modeling, dictionaries, and language modeling in speech recognition systems.

The representatives of these classes can often be deduced from a Bayesian network that extends the conventional hidden Markov models used in speech recognition.

As mentioned earlier, the proposed framework can be used to model context in speech as well as noise.

Because acoustic models in the speech recognizer are designed to represent their own acoustic-phonetic units, they can provide a much more detailed representation of speech.

In most cases, the acoustic model adaption is focused on the mean vectors and covariance matrices of the acoustic models in the speech recognizer due to their dominant effectiveness compared with other model parameters [1, 2].

It is then assumed that HEQ-MA is applied to each mean vector of all trained acoustic models in the speech recognizer on a component-by-component basis as in HEQ-FC.

HMMs have been widely used in automatic speech recognition (e.g., [35]) to model variability in speech caused by different speakers, speaking styles, vocabularies, and environments.