Sentence examples for input speech from inspiring English sources

The basis noise model is obtained from the silence segments of the input speech.

Initially, the spectral features are extracted from the input speech signal.

In this paper we introduce a scaled speech coder, which operates on time-scale modified input speech.

Thus, the resulting system is capable of animating faces realistically from input speech of any Turkish speaker.

Speaker verification performance degrades when input speech is tested in different sessions over a long period of time chronologically.

In this paper we introduce a novel approach to vocoders by coding the time-scale modified input speech signal.

For further syntactic and semantic level processing of the input speech in ASR, clause and sentence boundary detection and modality (sentence type) recognition was implemented.

For this, HMMs for the different types of word-stress unit contours were trained and then used for recognition and alignment of such units from the input speech.

The syntactic level input speech segmentation algorithm was evaluated for the Hungarian and for the Finnish languages that have fixed stress on the first syllable.

This prosodic segmentation of the input speech also allows word-boundary recovery and can be used for N-best lattice rescoring based on prosodic information.

The proposed structure maps an input speech utterance into one of the multiple emotion classes through subsequent layers of binary classifications.