Sentence examples for model utterance from inspiring English sources

Specifying the design is charting out the interaction model, utterance by utterance, schema by schema, with all the contingencies of system behavior represented in detail.

We analyzed 230 speech imitations of Koshik and 100 human model utterances.

Instead, they cluster tightly with the human model utterances, which were recorded from Koshik's trainers.

According to the prevalent modeling, utterance generation proceeds at several processing levels [1], [2].

The choice of back-end is dictated by the requirement that it should be capable of modelling utterances represented by a sequence of vectors to capture longer-term temporal information, and hence hidden Markov models (HMMs) were chosen.

Firstly, it is evident from the surveyed literature that, contrary to the assumptions of some experimental pragmaticists, there is no one, unique 'default model' of utterance interpretation.

We describe a frame-based formulation to SER that relies on minimal speech processing and end-to-end deep learning to model intra-utterance dynamics.

The digit 0 has an additional model for the utterance "oh".

First, they show that a considerable WER reduction of the baseline is achieved by employing the two acoustic models provided by REVERB Challenge and exploiting our estimate of C 50 to select the most appropriate model for each utterance between them (i.e. Clean&Multi cond).. Further improvement is achieved by training more reverberant models.

Clustering within continuous feature spaces is used to learn object property and utterance models from processed sensor data, forming a symbolic description.

We describe a robust system for numeric recognition and present algorithms for feature extraction, acoustic and language modeling, discriminative training, utterance verification and numeric understanding and validation.