2 results about "Vocal organ" patented technology

The application relates to the technical field of speech recognition, in particular to a speech-based emotion recognition method, which comprises the following steps: according to an input speech signal, frame division and windowing are carried out. The application can obtain more profound insights into emotional expression by decomposing the input speech signal into two physical sources of glottal excitation and vocal tract response for independent modeling and analysis, obtaining a vocal tract transfer function set via linear predictive coding operation, and applying inverse filtering to the original speech signal to reconstruct an approximate glottal pulse sequence, effectively stripping the influence of vocal tract resonance on the signal, so that perturbation parameters, open quotient and closed quotient and the like representing vocal cord vibration patterns can be directly calculated, at the same time, the vocal tract transfer function set is used to identify and track the dynamic trajectory of the formant, and the phase difference cosine mean between adjacent frames is combined to quantify the sound production stability, and subtle dynamic adjustment and control stability of sound production organs such as the oral cavity and tongue position caused by emotional changes are captured.

The application relates to a multilingual automatic recognition method and system, and belongs to the technical field of language recognition. The recognition method comprises the following steps: receiving an original voice signal and performing pretreatment to obtain a pretreated voice frame sequence; simultaneously extracting an acoustic feature vector, a vocal organ movement feature matrix and a prosody feature vector from the voice frame sequence to form a feature triple; performing language family classification according to the acoustic feature vector and the vocal organ movement feature matrix to output a candidate language family set; inputting the candidate language family set and the prosody feature vector into a dialect clustering model to output a refined dialect cluster label; calculating acoustic feature weight values, vocal organ movement feature weight values and prosody feature weight values, performing weighted operation on the feature triple to generate a weighted feature vector; and inputting the weighted feature vector and the refined dialect cluster label into a language decision model to output a language recognition result containing a language label and a confidence value. The application improves the accuracy and robustness of multilingual recognition in a complex environment.