Patent TW-202141467-A (TW)
Inventor: CHEN BO-SHOE
IPC: G10L17/00
Filed: 2020-04-29
Abstract: As indicated in our pending patent application of AiHear invention (No. 108132835), hearing echo of own voice is among one of the worst user experiences, even with the modern, specialized, custom-made, and the most expensive hearing aids devices. It is obvious that "Own Voice Cancellation (OVC)" is a highly-desired feature in hearing aids. Also indicated in another our patent application of AiRSIS invention (No. 108133997), recognition of the speaker in real-time is a must-have feature for OVC inside the hearing aids devices. This invention DCSI (Dual-class Speaker Identification for Hearing-Aids) describes a set of statistical classification algorithms, for further enhancements (accuracy and real-time speed) of the speaker identification for hearing-aids device. A general-purposed "Speaker Identification/Recognition/Diarization" usually requires a long-period (15 seconds or more) to identify a speaker and with only a so-so accuracy. Such algorithms are obviously NOT suitable for VOC in hearing-aids device. This DCSI invention achieves higher accuracy and faster real-time with a very important observation: we only need to classify Speakers into two classes, Master class, who wears the hearing-aids, and Guest class for all other people and it is irrelevant to further identify any specific speaker inside the Guest class. Based on this observation, this invention is able to reduce the complexity of Speaker Identification problem from a "multiple choice" to a simpler binary "true-false selection". This reduction of complexity leads obviously to much better accuracy and shortened real-time in the OVC process. The following algorithms describe the essence of this invention: 1. Reduce the number of classes into only two: Master and Guests. Train each class to extract their respective VoiceID, based on MFCC (Mel Frequency Ceptrum Coefficients) and other unique voice features cited in the academia literatures, including ZCR (Zero Crossing Rate), Speech Energy, Spread, Entropy, Flux, etc., in both time domain and frequency domain. 2. Separate the training data into many small samples, each with only one-second duration. Each one-second sample is further separated into 50 millisecond frames to extract features for VoiceID. Normalize all features in every sample and class, with (Norm, MEAN, STD) pair. A short duration of sample and frame are very important so that its pattern "simulates" better for faster recognition in real-time. 3. During the real conversation, use Master's MEAN and STD, instead of the total, global population values, to analyze the speech and to "discriminately" identify the speaker. Since the hearing-aids is placed closer to Master, the identification accuracy is very high, with FRR (False Rejection Rate) close to almost zero. 4. Train more Guest to acquire more number of samples than Master's. With much more samples, during the real conversation, Guest's speech is recognized correctly, even with Master's MEAN and STD, disadvantageously. In this way, the FAR (False Acceptance Rate) becomes very low, also closing to zero. 5. For real-time processing, the starting speech (talking stage, from silence to talk) is usually too short to identify speaker correctly. We resolve this problem by initializing the "talking stage" with pre-loaded features using MEAN values of all samples, instead of all zeros. Within a very short duration of real speech, the pre-loaded "virtual speech" after mixing with a minimum number of real speech blocks, proves to be very effective for speaker identification: it identifies the correct speaker almost immediately with very short delay. Preliminary test results show DCSI with the algorithm above is able to reach very accurate (99%) Speaker Identification for hearing-aids, and with minimal delay in less than 200ms.