Lucent Technologies Inc.

Alcatel-lucent USA Inc.

Alcatel Lucent

Doh-Suk Kim

Ahmed A Tarraf

Binshi Cao

Peter Kroon

Donald J Youtkus

Oded Ghitza

Donald Joseph Youtkus

A method and apparatus for use in suppressing acoustic echo in a target speech signal being transmitted through a packet-based communications network uses frame energy estimation applied to the target speech signal and to a reference speech signal. The method or apparatus estimates one or more reference speech energy levels in one or more reference packets based on one or more of the speech parameters generated by the speech encoding of the reference signal; estimates a target speech energy level in a target packet based on one or more of the speech parameters generated by the speech encoding of the target signal; compares the target speech energy level to one or more reference speech energy levels; and detects an echo in the target speech signal based on the comparison of the target speech energy level to the one or more reference speech energy levels.

Method and apparatus for the detection and suppression of echo in packet based communication networks using frame energy estimation

In one embodiment, distortion in a received speech signal is estimated using at least one model trained based on subjective quality assessment data. A speech quality assessment for the received speech signal is then determined based on the estimated distortion.

Speech quality assessment method and system

In a method for echo suppression or cancellation, a reference voice packet is selected from a plurality of reference voice packets based on at least one encoded voice parameter associated with each of the plurality of reference voice packets and the targeted voice packet. Echo in the targeted packet is suppressed or cancelled based on the selected reference voice packet.

Packet based echo cancellation and suppression

A method for objective speech quality assessment that accounts for phonetic contents, speaking styles or individual speaker differences by distorting speech signals under speech quality assessment. By using a distorted version of a speech signal, it is possible to compensate for different phonetic contents, different individual speakers and different speaking styles when assessing speech quality. The amount of degradation in the objective speech quality assessment by distorting the speech signal is maintained similarly for different speech signals, especially when the amount of distortion of the distorted version of speech signal is severe. Objective speech quality assessment for the distorted speech signal and the original undistorted speech signal are compared to obtain a speech quality assessment compensated for utterance dependent articulation.

Compensation for utterance dependent articulation for speech quality assessment

Disclosed is an objective speech quality assessment technique that reflects the impact of distortions which can dominate overall speech quality assessment by modeling the impact of such distortions on subjective speech quality assessment, thereby, accounting for language effects in objective speech quality assessment.

Method of reflecting time/language distortion in objective speech quality assessment

Auditory-articulatory analysis for use in speech quality assessment. Articulatory analysis is based on a comparison between powers associated with articulation and non-articulation frequency ranges of a speech signal. Neither source speech nor an estimate of the source speech is utilized in articulatory analysis. Articulatory analysis comprises the steps of comparing articulation power and non-articulation power of a speech signal, and assessing speech quality based on the comparison, wherein articulation and non-articulation powers are powers associated with articulation and non-articulation frequency ranges of the speech signal.

Auditory-articulatory analysis for speech quality assessment

A mapping function is generated between subjective measures of audio signal quality, e.g., mean opinion score (MOS) or degradation MOS (DMOS) measures, and corresponding objective distortion measures, e.g., auditory speech quality measures (ASQMs) or perceptual speech quality measures (PSQMs), for known audio signals. The subjective measures and corresponding objective distortion measures are determined in accordance with modulated noise reference unit (MNRU) conditions or other suitable distortion conditions placed on the source speech, and a regression analysis is applied to the results to generate the mapping function. The mapping function may then be utilized, e.g., to evaluate speech quality of additional source speech from a particular speech coding system. In this case, the objective distortion measure is generated using the additional source speech, and the resulting objective measure is applied as an input to the mapping function to generate an estimate of the value of the subjective measure. Advantageously, the mapping function is database-independent, and can thus be used, e.g., to generate accurate estimates of subjective measures of speech quality for speech databases unrelated to those used in generating the mapping function.

Growing community of inventors

Basking Ridge, NJ, United States of America

Doh-Suk Kim