Sentence examples for a word sequence from inspiring English sources

This system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the signs).

The speech recognizer is used for decoding the spoken utterance into a word sequence.

Given a word sequence s ̄, denote the t-th word as w t.

An input utterance is decoded into a word sequence Ŵ from an ASR.

Typical speech recognition uses a LM to introduce linguistic restrictions that helps the recognizer figure out a word sequence.

As a hybrid RNNLM can take two types of input units, the training text consists of two sets: a word sequence set and a PM sequence set.

We first recognize the input without using a DAE and obtain a best word sequence hypothesis.

While using a context-dependent FLM for a given word sequence, we decide which model order we want to use.

In statistical approaches, we take a probabilistic viewpoint of language modeling and estimate the probability P W) for a given word sequence W = w1w2,..., w n.

Language models are used to estimate the probability of a given word sequence W=(w 1,w 2,…,w n ).

Acoustic models, typically continuous density hidden Markov models (HMM) representing phones, are used to compute the probability of the acoustic material for a given word sequence,.