Constrained discriminative speaker verification specific to normalized i-vectors

International audienceThis paper focuses on discriminative trainings (DT) applied to i-vectors after Gaussian probabilistic linear discriminant analysis (PLDA). If DT has been successfully used with non-normalized vectors, this technique struggles to improve speaker detection when i-vectors have been first normalized, whereas the latter option has proven to achieve best performance in speaker verification. We propose an additional normalization procedure which limits the amount of coefficient to discriminatively train, with a minimal loss of accuracy. Adaptations of logistic regression based-DT to this new configuration are proposed, then we introduce a discriminative classifier for speaker verification which is a novelty in the field

Exploiting Uncertainty Information in Speaker Verification and Diarization

Tato práce se zabývá dvěma modely, které umožňují využít informace o nejistotě v úlohách automatického ověřování mluvčího a diarizace mluvčích. První model, který zvažujeme, je modifikací široce používané gaussovské pravděpodobnostní lineární diskriminační analýzy (G-PLDA), modelující rozložení vektorových reprezentací promluv nazývaných embeddingy. V G-PLDA se předpokládá, že embeddingy jsou generovány přidáním šumového vektoru navzorkovaného z Gaussova rozložení k vektoru reprezentujícímu mluvčího. Ukazujeme, že za předpokladu, že šum byl místo toho vzorkován ze Studentova T-rozdělení, model PLDA (tuto verzi nazýváme PLDA s těžkým chvostem, heavy-tail, HT-PLDA) může při rozhodnutí o ověření mluvčího využít informace o nejistotě. Náš model je koncepčně podobný modelu HT-PLDA definovanému Kennym et al. v roce 2010, ale jak ukazujeme v této práci, umožňuje rychlé skórování, zatímco původní definice HT-PLDA je značně časové a výpočetně náročná. Představujeme algoritmus pro trénování naší verze HT-PLDA jako generativního modelu a zvažujeme rovněž různé strategie diskriminativního trénování parametrů tohoto modelu. Generativně a diskriminativně trénovanou HT-PLDA testujeme na úloze ověřování mluvčího. Výsledky naznačují, že HT-PLDA funguje podobně jako standardní G-PLDA, přičemž má výhodu v odolnosti vůči změnám v předzpracování dat. Experimenty s diarizací mluvčích ukazují, že HT-PLDA poskytuje nejen lepší výsledky než základní G-PLDA, ale skóre logaritmického poměru věrohodností (log-likelihood ratio, LLR) produkovaná tímto modelem jsou lépe kalibrována. Ve druhém modelu nepovažujeme (na rozdíl od HT-PLDA) embeddingy za pozorovaná data. Místo toho jsou v tomto modelu embeddingy normálně rozložené skryté proměnné. Přesnost (precision) embeddingů nese informaci o kvalitě řečového segmentu: u čistých dlouhých segmentů by přesnost měla být vysoká a u krátkých a zašuměných promluv by měla být nízká. Ukazujeme, jak lze takové pravděpodobnostní embeddingy začlenit do skórování založeného na G-PLDA, a jak parametry skrytého embeddingu ovlivňují jeho vliv při výpočtu věrohodností s tímto modelem. V experimentech demonstrujeme, jak lze využít existující extraktor embeddingů založený na neuronové síti (NN) k produkci nikoli embeddingu, ale parametrů pravděpodobnostního rozložení embeddingu. Pravděpodobnostní embeddingy testujeme na úloze diarizace mluvčích. Výsledky ukazují, že tento model poskytuje dobře kalibrovaná skóre LLR umožňující lepší diarizaci, pokud není k dispozici vývojová datová sada pro ladění shlukovacího algoritmu.This thesis considers two models allowing to utilize uncertainty information in the tasks of Automatic Speaker Verification and Speaker Diarization. The first model we consider is a modification of the widely-used Gaussian Probabilistic Linear Discriminant Analysis (G-PLDA) that models the distribution of the vector utterance representations called embeddings. In G-PLDA, the embeddings are assumed to be generated by adding a noise vector sampled from a Gaussian distribution to a speakerdependent vector. We show that when assuming that the noise was instead sampled from a Student's T-distribution, the PLDA model (we call this version heavy-tailed PLDA) can use the uncertainty information when making the verification decisions. Our model is conceptually similar to the HT-PLDA model defined by Kenny et al. in 2010, but, as we show in this thesis, it allows for fast scoring, while the original HT-PLDA definition requires considerable time and computation resources for scoring. We present the algorithm to train our version of HT-PLDA as a generative model. Also, we consider various strategies for discriminatively training the parameters of the model. We test the performance of generatively and discriminatively trained HT-PLDA on the speaker verification task. The results indicate that HT-PLDA performs on par with the standard G-PLDA while having the advantage of being more robust against variations in the data pre-processing. Experiments on the speaker diarization demonstrate that the HT-PLDA model not only provides better performance than the G-PLDA baseline model but also has the advantage of producing better-calibrated Log-Likelihood Ratio (LLR) scores. In the second model, unlike in HT-PLDA, we do not consider the embeddings as the observed data. Instead, in this model, the embeddings are normally distributed hidden variables. The embedding precision carries the information about the quality of the speech segment: for clean long segments, the precision should be high, and for short and noisy utterances, it should be low. We show how such probabilistic embeddings can be incorporated into the G-PLDA framework and how the parameters of the hidden embedding influence its impact when computing the likelihood with this model. In the experiments, we demonstrate how to utilize an existing neural network (NN) embedding extractor to provide not embeddings but parameters of probabilistic embedding distribution. We test the performance of the probabilistic embeddings model on the speaker diarization task. The results demonstrate that this model provides well-calibrated LLR scores allowing for better diarization when no development dataset is available to tune the clustering algorithm.

Open-set Speaker Identification

This study is motivated by the growing need for effective extraction of intelligence and evidence from audio recordings in the fight against crime, a need made ever more apparent with the recent expansion of criminal and terrorist organisations. The main focus is to enhance open-set speaker identification process within the speaker identification systems, which are affected by noisy audio data obtained under uncontrolled environments such as in the street, in restaurants or other places of businesses. Consequently, two investigations are initially carried out including the effects of environmental noise on the accuracy of open-set speaker recognition, which thoroughly cover relevant conditions in the considered application areas, such as variable training data length, background noise and real world noise, and the effects of short and varied duration reference data in open-set speaker recognition. The investigations led to a novel method termed “vowel boosting” to enhance the reliability in speaker identification when operating with varied duration speech data under uncontrolled conditions. Vowels naturally contain more speaker specific information. Therefore, by emphasising this natural phenomenon in speech data, it enables better identification performance. The traditional state-of-the-art GMM-UBMs and i-vectors are used to evaluate “vowel boosting”. The proposed approach boosts the impact of the vowels on the speaker scores, which improves the recognition accuracy for the specific case of open-set identification with short and varied duration of speech material

Automatic assessment of motivational interview with diabetes patients

Diabetes cost the UK NHS £10 billion each year, and the cost pressure is projected to get worse. Motivational Interviewing (MI) is a goal-driven clinical conversation that seeks to reduce this cost by encouraging patients to take ownership of day-to-day monitoring and medication, whose effectiveness is commonly evaluated against the Motivational Interviewing Treatment Integrity (MITI) manual. Unfortunately, measuring clinicians’ MI performance is costly, requiring expert human instructors to ensure the adherence of MITI. Although it is desirable to assess MI in an automated fashion, many challenges still remain due to its complexity. In this thesis, an automatic system to assess clinicians adherence to the MITI criteria using different spoken language techniques was developed. The system tackled the chal- lenges using automatic speech recognition (ASR), speaker diarisation, topic modelling and clinicians’ behaviour code identification. For ASR, only 8 hours of in-domain MI data are available for training. The experiments with different open-source datasets, for example, WSJCAM0 and AMI, are presented. I have explored adaptative training of the ASR system and also the best training criterion and neural network structure. Over 45 minutes of MI testing data, the best ASR system achieves 43.59% word error rate. The i-vector based diarisation system achieves an F-measure of 0.822. The MITI behaviour code classification system with manual transcriptions achieves an accuracy of 78% for Non Question/Question classification, an accuracy of 80% for Open Question/Closed Question classification and an accuracy of 78% for MI Adherence and MI Non-Adherence classification. Topic modelling was applied to track whether the conversation segments were related to ‘diabetes’ or not on manual transcriptions as well as ASR outputs. The full automatic assessment system achieve an Assessment Error Rate of 22.54%. This is the first system that targets the full automation of MI assessment with reasonable performance. In addition, the error analysis from each step is able to guide future research in this area for further improvement and optimisation