Matrix Differential Calculus with Applications in Statistics and Econometrics

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

The various methods and devices described herein relate to devices which, in at least certain embodiments, may include one or more sensors for providing data relating to user activity and at least one processor for causing the device to respond based on the user activity which was determined, at least in part, through the sensors. The response by the device may include a change of state of the device, and the response may be automatically performed after the user activity is determined.

Automated Response to and Sensing of User Activity in Portable Devices

Abstruct- We have compared the performance of four approaches for automatic language identification of speech utterances: Gaussian mixture model (GMM) classification; single-language phone recognition followed by languagedependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses multiple single-language phone recognizers, each trained in a different language; and languagedependent parallel phone recognition (PPR). These approaches, which span a wide range of training requirements and levels of recognition complexity, were evaluated with the Oregon Graduate Institute Multi-Language Telephone Speech Corpus. Systems containing phone recognizers performed better than the simpler GMM classifier. The top-performing system was parallel PRLM, which exhibited an error rate of 2% for 45-s utterances and 5% for 10-s utterances in two-language, closed-set, forcedchoice classification. The error rate for 11-language, closed-set, forced-choice classification was 11 % for 45-s utterances and 21% for 10-s utterances.

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Comparison of four approaches to automatic language identification of telephone speech

Speaker diarization is the task of determining “who spoke when?” in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. Initially, it was proposed as a research topic related to automatic speech recognition, where speaker diarization serves as an upstream processing step. Over recent years, however, speaker diarization has become an important key technology for many tasks, such as navigation, retrieval, or higher level inference on audio data. Accordingly, many important improvements in accuracy and robustness have been reported in journals and conferences in the area. The application domains, from broadcast news, to lectures and meetings, vary greatly and pose different problems, such as having access to multiple microphones and multimodal information or overlapping speech. The most recent review of existing technology dates back to 2006 and focuses on the broadcast news domain. In this paper, we review the current state-of-the-art, focusing on research developed since 2006 that relates predominantly to speaker diarization for conference meetings. Finally, we present an analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research.

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Speaker Diarization: A Review of Recent Research

In conventional speaker recognition method based on MFCC, the phase information has been ignored. In this paper, we proposed a method that integrates the phase information on a speaker recognition method. The speaker identification experiments were performed using NTT database which consists of sentences uttered at normal speed mode by 35 Japanese speakers (22 males and 13 females) on five sessions over ten months. Each speaker uttered only 5 training utterances (about 20 seconds in total). Using the phaseinformation, the speaker recognition error rate was reduced by about 44%. Index Terms: speaker identification, MFCC, phase information, GMM, combination method

Speaker Recognition by Combining MFCC and Phase Information

In conventional speaker recognition methods based on Mel-frequency cepstral coefficients (MFCCs), phase information has hitherto been ignored. In this paper, we propose a phase information extraction method that normalizes the change variation in the phase according to the frame position of the input speech and combines the phase information with MFCCs in text-independent speaker identification and verification methods. There is a problem with the original phase information extraction method when comparing two phase values. For example, the difference in the two values of π-\mathtildeθ1 and \mathtildeθ2=-π+\mathtildeθ1 is 2π-2\mathtildeθ1 . If \mathtildeθ1 ≈ 0, then the difference ≈ 2π, despite the two phases being very similar to one another. To address this problem, we map the phase into coordinates on a unit circle. Speaker identification and verification experiments are performed using the NTT database which consists of sentences uttered by 35 (22 male and 13 female) Japanese speakers with normal, fast and slow speaking modes during five sessions. Although the phase information-based method performs worse than the MFCC-based method, it augments the MFCC and the combination is useful for speaker recognition. The proposed modified phase information is more robust than the original phase information for all speaking modes. By integrating the modified phase information with the MFCCs, the speaker identification rate was improved to 98.8% from 97.4% (MFCC), and equal error rate for speaker verification was reduced to 0.45% from 0.72% (MFCC), respectively. We also conducted the speaker identification and verification experiments on a large-scale Japanese Newspaper Article Sentences (JNAS) database, a similar trend as NTT database was obtained.

Speaker Identification and Verification by Combining MFCC and Phase Information

The detection of human and spoofed (synthetic/converted) speech has started to receive more attention. In this study, relative phase information extracted from a Fourier spectrum is used to detect human and spoofed speech. Because original/natural phase information is almost entirely lost in spoofed speech using current synthesis/conversion techniques, a modified group delay based feature, the frequency derivative of the phase spectrum, has been shown effective for detecting human speech and spoofed speech. The modified group delay based phase contains both the magnitude spectrum and phase information. Therefore, the relative phase information, which contains only phase information, is expected to achieve a better spoofing detection performance. In this study, the relative phase information is also combined with the Mel-Frequency Cepstral Coefficient (MFCC) and modified group delay. The proposed method was evaluated using the “ASVspoof 2015: Automatic Speaker Verification Spoofing and Countermeasures Challenge” dataset. The results show that the proposed relative phase information significantly outperforms the MFCC and modified group delay. The equal error rate (EER) was reduced from 1.74% of MFCC, 0.83% of modified group delay to 0.013% of relative phase. By combining the relative phase with MFCC and modified group delay, the EER was reduced to 0.002%. Index Terms: Spoofing detection, relative phase information, group delay, GMM, countermeasures

Relative phase information for detecting human speech and spoofed speech.

Addresses the problem of the detection of speaker changes and clustering speakers when no information is available regarding speaker classes or even the total number of classes. We assume that no previous information on speakers is available (no speaker model, no training phase) and that people do not speak simultaneously. The aim is to apply speaker grouping information to speaker adaptation for speech recognition. We use vector quantization (VQ) distortion as the criterion. A speaker model is created from successive utterances as a codebook by a VQ algorithm, and the VQ distortion is calculated between the model and an utterance. A result was obtained by the experiment on speaker detection and speaker clustering. The speaker change detection experiment was compared with results by generalized likelihood ratio and Bayesian information criterion. We show the superiority of our proposed method.

Speaker change detection and speaker clustering using VQ distortion for broadcast news speech recognition

In conventional speaker recognition methods based on MFCC, the phase information has been ignored. Recently, we proposed a method that integrated MFCC with the phase information on a speaker recognition method. Using the phase information, the speaker identification error rate was reduced by 78% for clean speech. In this paper, we describe the effectiveness of phase information for noisy environments on speaker identification. Integrationg MFCC with phase information, the speaker error identification rates were reduced by 20%∼70% in comparison with using only MFCC in noisy environments.

Seiichi Nakagawa

Papers

Speaker Recognition by Combining MFCC and Phase Information

Speaker Identification and Verification by Combining MFCC and Phase Information

Relative phase information for detecting human speech and spoofed speech.

Speaker change detection and speaker clustering using VQ distortion for broadcast news speech recognition

Speaker identification by combining MFCC and phase information in noisy environments