Sentence examples for training speech from inspiring English sources

When the computer was allowed to use the same person's training speech, it was fairly accurate around 75% of words spoken for all subjects and up to 97% for one speaker.

Here, N denotes the total number of training speech feature vectors, and S is the order of the polynomial function.

Therefore, a codebook of impulse response vectors generated from the training speech data is proposed as the sparsifying matrix in[5].

However, the mismatch in noise type and SNR value between test and training speech make it difficult for the MMSR to perform significantly better than the MTR method.

We first carry out forced alignments of training speech data with their transcriptions to obtain phone boundaries which define the phone templates.

The compensation will estimate the noisy log-spectrum in the training speech given the log-spectrum from the test speech using a statistical relation.

We train speech recognition models, entity extraction and a couple of other data mining activities.

In this section, an experimental investigation was performed to illustrate the effect of SNR mismatch between noisy test and train speech in MMSR.

For the two databases, each sound file was manually labelled twice, by musically trained speech therapists who distinguished prosodic occurrences as intonational groups and accents [32].

Showing result of SNR mismatch between noisy test and train speech for four noise types in the Aurora 2 database (The red line connects the minimum WER points).

Later, NNs were also applied in ASR front ends for extracting discriminatively trained speech features (denoted as tandem [17] or bottleneck (BN) [10] approaches).