Sentence examples for sources of speech from inspiring English sources

The gender of the speaker is one of the influential sources of speech variability.

This chapter provides an overview of an automatic speech recognition system and describes sources of speech variability that cause mismatch between training and testing.

Human machine interaction for all of these areas requires the existence of speech analysis, speech recognition, and speech verification algorithms that are robust with respect to the sources of speech variability that are characteristic of this population of speakers.

The development of these interactive tools along with the underlying speech technologies that support them requires the existence of speech processing, whose algorithms must be robust with respect to the sources of speech variability that are characteristic of this population of speakers.

The sound sources of speech production are pulses of air expelled into the vocal tract by adduction and vibration of the vocal folds.

The debates provide a source of speech from multiple candidates (we consider eight of the 10 who participated) in a similar environment and over an extended period (20 debates spanning from June 2011 to February 2012).

Apparently, the underlying assumption to do so is that deficits in the auditory periphery are the most apparent source of speech perception difficulties.

Some of these reasons can be stated: environmental conditions are (usually) rapidly changing or highly degraded, acquisition processes are not always under control, incriminated people exhibit low degree of cooperativeness, etc., inducing a wide range of variability sources on speech utterances.

This approach has been employed for robust source decoding of speech signals [18-23] [18-23]-coded audio signals [24,25], and uncompressed audio [26] thaudioploit signalsredundancy in sample values or various source codec parameters (e. g., scaling factors, line spectral frequencies (LSFs) vectors, vector-quantized gains, adaptive codebook indices).

The recordings were labeled with P for presentations/orations T for translations, R for radio dialogues, and A for audiobooks and other sources of read speech and described with number of the speaker and duration of utterance (in minutes).

The proposed algorithm with its improved SIR using harmonic alignment and efficient computational complexity is suitable for hardware implementation for the real-time blind source separation of speech signals.