In PublicationI, we evaluated the proposed speaker diarization system using the NIST SRE 2008 [160] telephone data. Since the goal of the experiment was to study the properties of speaker clustering algorithms, rather than comparing complete
1Members of the “HAPPY” team are the authors of the paperIII.
Table 6.1: Summary of the speech corpora. SRE: speaker recognition evaluation, DAC: domain adaptation challenge, SAD: speech activity detection.
Corpus Description Used
in SRE’08
[160]
The data used in our experiments is a subset of the NIST SRE 2008 corpus. It consists of 2,215 telephone conversations, each involving two speakers, and approximately 5 minutes in duration. The total duration is approximately 200 hours.
I
DAC [216] The experimental setup of DAC involves two separate training sets:in-domain(SRE) andout-of-domain(SWB). The in-domain SRE set consists of telephone calls from 3790 speakers (male and female) and 36470 speech cuts taken from the SRE 04, 05, 06 and 08 collections. The out-of-domain SWB set consists of telephone calls from 3114 speakers (male and female) and 33039 speech cuts taken from Switchboard-I and II corpora [186]. The SRE’10 telephone data is used as the enrollment and test sets. The average duration of audio recordings in both the training and the test data is 5 minutes. Note: this dataset contains no actual speech data — all the recordings are represented by i-vectors.
II
OpenSAD [120]
The OpenSAD data originates from one of the DARPA RATS (Robust Automatic Transcription of Speech) evaluation sets [217]. It consists of highly degraded recordings obtained by transmitting the source audio over several different noisy radio communication channels. We included data from the dev-2 subset of the official development part in our evaluation set, resulting in 661 audio recordings with an average duration of 10 minutes. The total duration is approximately 100 hours.
III,IV
SRE’10 [151]
We used the NIST SRE 2010 dataset to evaluate the i-vector based speaker verification system using different SAD methods. In specific, we used a subset of the male trials from the normal vocal effort telephone speech condition (CC5) consisting of telephone calls of approximately 5 minutes in duration.
IV
RSR2015 [218]
The RSR2015 corpus consists of microphone speech collected in clean environmental condition using a set of six portable devices including smart phones and tablets. The corpus comprises over 71 hours of speech recorded from English speakers in Singapore. The pool of speakers consists of 300 participants (157 male and 143 female speakers). Each of the participants recorded 9 sessions consisting of 30 short sentences.
IV
RedDots [219]
In our experiments we used the quarter four (Q4) release of RedDots corpus. The corpus is collected from speakers using different smartphones in different environmental conditions. The pool of speakers consists of 300 participants (49 male and 13 female speakers) from 21 countries. The total number of sessions in the Q4 release is 572 (473 male and 99 female sessions).
IV
speaker diarization systems, we usedoraclespeech activity boundaries to ignore er-rors caused by a speech activity detector. As a result, two out of the three terms of the DER metric, namely, missed speech rate and false alarm speech rate vanish.
This allows one to focus on only speaker confusion error, which relates to the per-formance of speaker clustering. Evaluation of the proposed system yielded DERs varying from 1.74% to 2.35%, while the baseline eigenvoice based system [42], un-der comparable conditions, has a lower DER of 1.29%. However, [42] requires the number of speakers to be known in advance while in the proposed system this is determines automatically; experimentally, the proposed system detected the correct number of speakers in more than 90% of conversations.
In Publication II, we evaluated the proposed classifier, mt-PSVM, on the data from theDomain Adaptation Challenge(DAC) [216] organized by Johns Hopkins Uni-versity. In the experiments by the author, mt-PSM reduced the EER from 6.88% to 4.18% in a gender-independent setup when trained on the out-of-domain Switch-board [186] dataset and tested on the in-domain SRE 2010 [151] dataset. Even if the new proposal, mt-PSVM, did not outperform a competing technique, inter dataset variability compensation(IDVC) [220] which achieved EER=3.08% on the same setup, it represents a different way to compensate inter-dataset variability for discrimina-tive formulation of the PLDA. The other existing techniques, including IDVC, are designed for generative formulation of the PLDA. As discussed in [221], the shared weight vector in mt-SVM does not necessarily yield an accurate classifier because it is not constrained to perform well on any task and is only used to share information across tasks. Thus, devising alternative objective functions or parameter sharing strategies might be a research direction worthy of further exploration.
In Publications III and IV, the author and his co-authors evaluated the pro-posed SAD method using the dataset released as a part of the NIST OpenSAD challenge [120]. We observed increased accuracy when unlabeled data was used to estimate speech and non-speech statistical models. Further, we conducted extensive speaker verification experiments with NIST SRE 2010 [151], RSR2015 [218], and Red-Dots [219] corpora. The results showed benefits of the proposed method especially on long speech recordings containing non-stationary noise. Finally, we studied how integration of the proposed SAD method into a downstream recognition application
— here, speaker verification — impacts recognition errors. We analyzed the depen-dence of the trade-off between misses and false alarms on the EER of the i-vector based ASV system. We observed that a higher miss rate is less of a problem than a higher false alarm rate. Therefore, false alarms should be penalized roughly 4 to 5 times more to get the best performance. That is, a speaker model estimated from a smaller portion of pure data is preferable to a noisy model.
A summary of the main contributions and achievements is provided in Table 6.2.
Table 6.2: Summary of the main results in publications. ASV: automatic speaker verification, SAD: speech activity detection, SD: speaker diarization, EER: equal er-ror rate, DCF: detection cost function
Application The core proposal Dataset Results
I SD Diarization with anunknownnumber of speakers.
SRE’08 New clustering method to accurately detect the number of speakers.
II ASV Compensation of inter-dataset variability via multi-task learning.
DAC EER: 6.88%→4.18%.
III SAD Introduction of unsupervised GMM-based SAD.
OpenSAD New method that contributes to fusion of SADs.
IV SAD Improvement of the SAD method from IIIto enable using all frames by adopting semi-supervised learning.
OpenSAD Up to 5% reduction in DCF compared to PublicationIII.
IV SAD &
ASV
Study of the impact of SAD method on ASV performance
SRE’10, RSR2015, RedDots.
The proposed SAD method is best suited for long and noisy data conditions.
IV SAD &
ASV
Determination of the optimal SAD miss and false alarm trade-off for ASV
SRE’10. SAD false alarms should be penalized 4 to 5 times more over the misses.
7 CONCLUSIONS
The work done in this thesis contributes to the improvement of machine learning techniques for automatic text-independent speaker recognition and speech segmen-tation. First, it addresses speaker diarization problem in the most general formula-tion, that is, when the number of speakers in a recorded conversation is unknown a priori. A probabilistic generative model for similarity (affinity) matrices was pro-posed and objectively evaluated on the NIST SRE 2008 telephone data using the commonly adopted protocol. The experiment indicated that the fully Bayesian treat-ment for determining model complexity allows the correct number of speakers to be accurately estimated. The downside is compromised segmentation accuracy com-pared to the best performing algorithm with a known (oracle) number of speakers.
One distinctive feature of the new model is that it can be naturally extended to the multi-view setting (when multiple similarity matrices are available) by using mul-tivariate distributions. Exploring the potential of combining multiple sources of information in the multi-view scenario would be an interesting direction for future work.
Second, a multi-task extension of the pairwise support vector machine was stud-ied with the aim of reducing the effect of domain mismatch between training and evaluation data in speaker verification. The evaluation results indicate that the pro-posed discriminative classifier reduces the performance gap caused by domain mis-match, though struggles to achieve accuracy similar to its generative counterparts.
The author believes that the proposed method can be improved by altering the ob-jective function and parameter sharing strategy.
Finally, the author studied a simple and general-purpose probabilistic speech activity detection method in which classes are modeled by Gaussian mixture mod-els. The author proposed a new training method based on semi-supervised learning paradigm that provides a methodology for estimating more accurate models by incorporating unlabeled data into the training process. We observed increased accu-racy of the stand-alone SAD system on the challenging dataset from the recent NIST OpenSAD evaluation. Our extensive automatic speaker verification (ASV) evalua-tion, including both text-independent experiments and text-dependent experiments, suggested benefits of the GMM based SAD method for the long speech segments.
Also, the evaluation results indicate comparable, on average, ASV performance with classic unsupervised SAD methods which are designed specifically for this task and exploit the information about the nature of speech signal. In contrast, the proposed SAD method makes no strong assumptions about the input data and is not tied, in principle, to any particular features. This leaves room for potential improvement by either constraining the model to take into account some specifics of the input data, or adopting alternative features with higher discriminative abilities.
In fact, output scores from other SADs can be used as the features enabling fusion of different SAD methods. Further exploration of this idea is left for future work.
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