Sentence examples for classification speech from inspiring English sources

Multi-class pattern classification has many applications including text document classification, speech recognition, object recognition, etc. Multi-class pattern classification using neural networks is not a trivial extension from two-class neural networks.

Classification of speech is one of the most vital problems in speech processing.

The classification of speech can be done into five categories depending upon the modes of speech production i.e. difference in vocal efforts[3].

The average performance in the classification of speech under stress was improved by 10%to15%5% using the proposed features, compared to traditional methods of stressed speech classification.

Affect or emotion classification from speech has much to benefit from ensemble classification methods.

Although there have been many studies on the classification of speech, the results are still limited.

This paper presents methods and results for the use of both boosting and bagging for phoneme classification and speech recognition.

CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data.

At this point (for ), the correct classification of speech was about 78% and the correct classification of noise was about 50% (see Figure 3).

As the first part, the polynomial regression, k-means classification, and speech enhancement applied on the filter bank outputs are explained in Sections 2.1, 2.2, 2.3, 2.4, and 2.5.

These instructions are shared between the different signal processing algorithms included in the device: the multi-band compression-expansion algorithm, feedback cancellation, automatic acoustic environment classification, and speech enhancement.