Sentence examples for populations of speakers from inspiring English sources

The 12 other dictionaries financed in recent years by the Living Tongues Institute, a nonprofit group, in partnership with the National Geographic Society — which helped start the Siletz dictionary project in 2005 and now uses it as a blueprint — are all centered on languages still in use, however small or threatened their populations of speakers may be.

In contrast, some languages with large populations of speakers, such as Mandarin, Hindi, and Arabic, are relatively isolated in these networks.

In addition, if linguists discovered that some linguistic feature was optional in two different languages, then even if psychological experiments showed differences between the two populations of speakers, this would not show linguistic determination or influence.

Demographic attraction is strongest between large population of speakers who live in separate counties which are located closely.

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.

By using the ratio of the average third-formant location for a particular speaker to the average third-formant location for a large population of speakers, they were able to determine reasonable normalization factors, which helped reduce interspeaker variations.

This is "the largest population of speakers of indigenous languages and presents the highest cultural diversity in the Americas, with regard to the number of languages spoken" (Terborg et al. 2006, p. 417).

Here, we define the following terms as in [7]: (i) denotes the set of feature vectors for all of the available utterances from speaker, warped by warp factor, (ii) denotes the set of transcriptions of all utterances, (iii) denotes the best warp factor for speaker, (iv) denotes a given HMM trained from a large 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.

If denotes a set of single-Gaussian HMM models trained from a large population of speakers, then the optimal warping factor for the th speaker, is obtained by maximizing the likelihood of the warped utterances with respect to the model and the transcription [14]: (13).

The most popular way of estimating VTLN warps is to use likelihood-based estimation techniques [6, 7] in which a set of HMM models trained on a large population of speakers by placing 1 Gaussian per state is scored against warped features.