Patent TW-202141468-A (TW)
Title: Vodsie: volume-aided discriminator for speaker identification enhancement
Inventor: CHEN BO-SHOE
IPC: G10L17/20
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. 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 hearing aids devices. This invention describes a volume-based algorithm, for the enhancement of the speaker-recognition accuracy and to resolve the “ending vowel sound” problem, found in Chinese-spoken language. This invention 
is called Volume-based Discriminator for Speaker Identification Enhancement, or VoDSIE in short. 
 
     MFCC (Mel Frequency Ceptrum Coefficient) has been recognized as “the most widely used”, among the state-of-the-art “speech/speaker recognition” technologies. Unfortunately, MFCC even though gets favorable results, its accuracy still has a lot of room for improvements, especially for Chinese-spoken language. One observation is that MFCC focuses only on the speaker’s frequency-domain characteristics. Speaker’s volume is actually quite important to 
differentiate different speakers, especially when the hearing-aids device is placed much closer to the user (called the “Master”) than to other people (called the “Guest”) in conversations. The difference in speakers’ volume power, when properly used, can differentiate Master from Guest faster and more accurately, which enhances speaker identification accuracy significantly. 
 
     Even though it appears to be continuous to human ears, any real speech is actually “segmented” with “ending vowel sound”, followed by a very silence period. This “ending vowel sound” is especially obvious in Chinese-spoken language, with distinctive word-boundary. This “ending vowel sound”, if included in MFCC, creates inaccuracy in speaker recognition. This invention uses speech power level (“root mean square”) to exclude and to ignore the “ending vowel sound”, which increases the accuracy of speaker identification significantly. 
 
     The VoDSIE algorithm is described as below: 
 
     1. Training Phase: 
 
     甲、Place Master’s cell phone at his upper pocket or somewhere else near him, where he usually talks comfortably. 
 
     乙、Record the Master’s speech at his regular tone and volume level, for about 120 seconds. 
 
     丙、Our software separates the Master’s speech into segments. 
 
     丁、Our software calculates the MEAN of different segments, including the volume level (root-mean-square). Our software also decides the “Ending Vowel Sound” level ratio relative to the MEAN, called EVR- End Vowel Ratio. 
 
     戊、Record a conversation between Master and Guest at the usual distance. 
 
     己、Our software calculates the sound level ratio between Master and Guest, called MGR- Master/Guest Ratio. 
 
     2. Real Conversation Phase: 
 
     甲、Take streaming, real-time short (typically, 20ms to 50ms) samples of voice from microphone input, and process them immediately, one by one at a time. 
 
     乙、Take a running window (size adjustable, 20 in typical cases) average of the 
samples’ volume, called ARV (Average Running Volume). 
 
     丙、Compare ARV with MEAN/MGR, and MEAN/EVR to decide whether it is Master, Guest, or End-of-Vowel. 
 
     丁、Smooth the results above to avoid “Abrupt or frequent State Changes). In general the “Smoother” will guarantee one segment one speaker. 
 
     Preliminary test results show VoDSIE with the algorithm above is able to reach almost 100% to discriminate Guest from Master. Moreover, the “ending vowel sound” problem, found in Chinese-spoken language conversation, can be resolved to reach more than 95% accuracy for Speaker Identification.