http://cs.uef.fi/sipu/pub/SRE-review-Kinnunen-and-Li.pdf

An overview of text-independent speaker recognition: From features to supervectors

http://www.cse.yorku.ca/~hj/mypubs/Jiang_sc05.pdf

Confidence measures for speech recognition: A survey

https://hal.archives-ouvertes.fr/hal-01926338/document

Text-dependent speaker verification: Classifiers, databases and RSR2015

Device, system, and method of liveness detection using voice biometrics. For example, a method comprises: generating a first matching score based on a comparison between: (a) a voice-print from a first text-dependent audio sample received at an enrollment stage, and (b) a second text-dependent audio sample received at an authentication stage; generating a second matching score based on a text-independent audio sample; and generating a liveness score by taking into account at least the first matching score and the second matching score.

Device, system, and method of liveness detection utilizing voice biometrics

This paper presents Mahimahi, a framework to record traffic from HTTP-based applications, and later replay it under emulated network conditions. Mahimahi improves upon prior record-and-replay frameworks in three ways. First, it is more accurate because it carefully emulates the multi-server nature of Web applications, present in 98% of the Alexa US Top 500 Web pages. Second, it isolates its own network traffic, allowing multiple Mahimahi instances emulating different networks to run concurrently without mutual interference. And third, it is designed as a set of composable shells, providing ease-of-use and extensibility.

We evaluate Mahimahi by: (1) analyzing the performance of HTTP/1.1, SPDY, and QUIC on a corpus of 500 sites, (2) using Mahimahi to understand the reasons why these protocols are suboptimal, (3) developing Cumulus, a cloud-based browser designed to overcome these problems, using Mahimahi both to implement Cumulus by extending one of its shells, and to evaluate it, (4) using Mahimahi to evaluate HTTP multiplexing protocols on multiple performance metrics (page load time and speed index), and (5) describing how others have used Mahimahi.

/pdf/mahimahi-accurate-record-and-replay-for-http-4cfqybyixf.pdf

Mahimahi: accurate record-and-replay for HTTP

In speech recognition, speech/non-speech detection must be robust to,noise. In the paper, a method for speech/non-speech detection using a linear discriminant analysis (LDA) applied to mel frequency cepstrum coefficients (MFCC) is presented. The energy is the most discriminant parameter between noise and speech. But with this single parameter, the speech/non-speech detection system detects too many noise segments. The LDA applied to MFCC and the associated test reduces the detection of noise segments. This new algorithm is compared to the one based on signal to noise ratio (Mauuary and Monne, 1993).

Robust speech/non-speech detection using LDA applied to MFCC

This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine similarity between bag-of-words, so as to get a soft-cosine that takes into account relations between words. According to the type of relation matrix embedded in the soft-cosine, semantic or lexical relations can be considered. Our system ranked first among the official submissions of subtask B.

SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering

During a learning phase, a speech recognition device generates parameters of an acceptance voice model relating to a voice segment spoken by an authorized speaker and a rejection voice model. It uses normalization parameters to normalize a speaker verification score depending on the likelihood ratio of a voice segment to be tested and the acceptance model and rejection model. The speaker obtains access to a service application only if the normalized score is above a threshold. According to the invention, a module updates the normalization parameters as a function of the verification score on each voice segment test only if the normalized score is above a second threshold.

Verification score normalization in a speaker voice recognition device

The overlapping speech detection systems developped by Orange and LIMSI for the ETAPE evaluation campaign on French broadcast news and debates are described. Using either cepstral features or a multi-pitch analysis, a F1-measure for overlapping speech detection up to 59.2% is reported on the TV data of the ETAPE evaluation set, where 6.7% of the speech was measured as overlapping, ranging from 1.2% in the news to 10.4% in the debates. Overlapping speech segments were excluded during the speaker diarization stage, and these segments were further labelled with the two nearest speaker labels, taking into account the temporal distance. We describe the effects of this strategy for various overlapping speech systems and we show that it improves the diarization error rate in all situations and up to 26.1% relative in our best configuration.

/pdf/impact-of-overlapping-speech-detection-on-speaker-2vsizqlkp9.pdf

Impact of overlapping speech detection on speaker diarization for broadcast news and debates

The invention concerns a method for identifying a speaker among participants distributed in groups (G1 to GI) in the proximity of telephone terminals (T1 to TI) by his/her voice during a teleconference managed by a conference device (PCT) connected to the terminals. After the speaker's voice has been identified in reception by the conference device, an identifier of the speaker's voice is broadcast from the conference device to the terminals and reproduced in the terminals. The speaker is introduced to the other participants, automatically or on request, for instance, by a voice or visual message comprising his/her name. The conference device may be included in one of the terminals; for example, the identifier identifies a speaker other than the communicating telephone or radiotelephone station owner.

Delphine Charlet

Papers

Robust speech/non-speech detection using LDA applied to MFCC

SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering

Verification score normalization in a speaker voice recognition device

Impact of overlapping speech detection on speaker diarization for broadcast news and debates

Identification of participant in a teleconference