Unsupervised adaptation of PLDA models for broadcast diarization

We present a novel model adaptation approach to deal with data variability for speaker diarization in a broadcast environment. Expensive human annotated data can be used to mitigate the domain mismatch by means of supervised model adaptation approaches. By contrast, we propose an unsupervised adaptation method which does not need for in-domain labeled data but only the recording that we are diarizing. We rely on an inner adaptation block which combines Agglomerative Hierarchical Clustering (AHC) and Mean-Shift (MS) clustering techniques with a Fully Bayesian Probabilistic Linear Discriminant Analysis (PLDA) to produce pseudo-speaker labels suitable for model adaptation. We propose multiple adaptation approaches based on this basic block, including unsupervised and semi-supervised. Our proposed solutions, analyzed with the Multi-Genre Broadcast 2015 (MGB) dataset, reported significant improvements (16% relative improvement) with respect to the baseline, also outperforming a supervised adaptation proposal with low resources (9% relative improvement). Furthermore, our proposed unsupervised adaptation is totally compatible with a supervised one. The joint use of both adaptation techniques (supervised and unsupervised) shows a 13% relative improvement with respect to only considering the supervised adaptation

Advances in Subspace-based Solutions for Diarization in the Broadcast Domain

La motivación de esta tesis es la necesidad de soluciones robustas al problema de diarización. Estas técnicas de diarización deben proporcionar valor añadido a la creciente cantidad disponible de datos multimedia mediante la precisa discriminación de los locutores presentes en la señal de audio. Desafortunadamente, hasta tiempos recientes este tipo de tecnologías solamente era viable en condiciones restringidas, quedando por tanto lejos de una solución general. Las razones detrás de las limitadas prestaciones de los sistemas de diarización son múltiples. La primera causa a tener en cuenta es la alta complejidad de la producción de la voz humana, en particular acerca de los procesos fisiológicos necesarios para incluir las características discriminativas de locutor en la señal de voz. Esta complejidad hace del proceso inverso, la estimación de dichas características a partir del audio, una tarea ineficiente por medio de las técnicas actuales del estado del arte. Consecuentemente, en su lugar deberán tenerse en cuenta aproximaciones. Los esfuerzos en la tarea de modelado han proporcionado modelos cada vez más elaborados, aunque no buscando la explicación última de naturaleza fisiológica de la señal de voz. En su lugar estos modelos aprenden relaciones entre la señales acústicas a partir de un gran conjunto de datos de entrenamiento. El desarrollo de modelos aproximados genera a su vez una segunda razón, la variabilidad de dominio. Debido al uso de relaciones aprendidas a partir de un conjunto de entrenamiento concreto, cualquier cambio de dominio que modifique las condiciones acústicas con respecto a los datos de entrenamiento condiciona las relaciones asumidas, pudiendo causar fallos consistentes en los sistemas.Nuestra contribución a las tecnologías de diarización se ha centrado en el entorno de radiodifusión. Este dominio es actualmente un entorno todavía complejo para los sistemas de diarización donde ninguna simplificación de la tarea puede ser tenida en cuenta. Por tanto, se deberá desarrollar un modelado eficiente del audio para extraer la información de locutor y como inferir el etiquetado correspondiente. Además, la presencia de múltiples condiciones acústicas debido a la existencia de diferentes programas y/o géneros en el domino requiere el desarrollo de técnicas capaces de adaptar el conocimiento adquirido en un determinado escenario donde la información está disponible a aquellos entornos donde dicha información es limitada o sencillamente no disponible.Para este propósito el trabajo desarrollado a lo largo de la tesis se ha centrado en tres subtareas: caracterización de locutor, agrupamiento y adaptación de modelos. La primera subtarea busca el modelado de un fragmento de audio para obtener representaciones precisas de los locutores involucrados, poniendo de manifiesto sus propiedades discriminativas. En este área se ha llevado a cabo un estudio acerca de las actuales estrategias de modelado, especialmente atendiendo a las limitaciones de las representaciones extraídas y poniendo de manifiesto el tipo de errores que pueden generar. Además, se han propuesto alternativas basadas en redes neuronales haciendo uso del conocimiento adquirido. La segunda tarea es el agrupamiento, encargado de desarrollar estrategias que busquen el etiquetado óptimo de los locutores. La investigación desarrollada durante esta tesis ha propuesto nuevas estrategias para estimar el mejor reparto de locutores basadas en técnicas de subespacios, especialmente PLDA. Finalmente, la tarea de adaptación de modelos busca transferir el conocimiento obtenido de un conjunto de entrenamiento a dominios alternativos donde no hay datos para extraerlo. Para este propósito los esfuerzos se han centrado en la extracción no supervisada de información de locutor del propio audio a diarizar, sinedo posteriormente usada en la adaptación de los modelos involucrados.<br /

Robust acoustic domain identification with its application to speaker diarization

International audienceWith the rise in multimedia content over the years, more variety is observed in the recording environments of audio. An audio processing system might benefit when it has a module to identify the acoustic domain at its front-end. In this paper, we demonstrate the idea of acoustic domain identification (ADI) for speaker diarization. For this, we first present a detailed study of the various domains of the third DIHARD challenge highlighting the factors that differentiated them from each other. Our main contribution is to develop a simple and efficient solution for ADI. In the present work, we explore speaker embeddings for this task. Next, we integrate the ADI module with the speaker diarization framework of the DIHARD III challenge. The performance substantially improved over that of the baseline when the thresholds for agglomerative hierarchical clustering were optimized according to the respective domains. We achieved a relative improvement of more than 5% and 8% in DER for core and full conditions, respectively, on Track 1 of the DIHARD III evaluation set

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

A Review of Deep Learning Techniques for Speech Processing

The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, with far-reaching implications for a range of industries and applications. This review paper provides a comprehensive overview of the key deep learning models and their applications in speech-processing tasks. We begin by tracing the evolution of speech processing research, from early approaches, such as MFCC and HMM, to more recent advances in deep learning architectures, such as CNNs, RNNs, transformers, conformers, and diffusion models. We categorize the approaches and compare their strengths and weaknesses for solving speech-processing tasks. Furthermore, we extensively cover various speech-processing tasks, datasets, and benchmarks used in the literature and describe how different deep-learning networks have been utilized to tackle these tasks. Additionally, we discuss the challenges and future directions of deep learning in speech processing, including the need for more parameter-efficient, interpretable models and the potential of deep learning for multimodal speech processing. By examining the field's evolution, comparing and contrasting different approaches, and highlighting future directions and challenges, we hope to inspire further research in this exciting and rapidly advancing field

Analyzing and Applying Cryptographic Mechanisms to Protect Privacy in Applications

Privacy-Enhancing Technologies (PETs) emerged as a technology-based response to the increased collection and storage of data as well as the associated threats to individuals' privacy in modern applications. They rely on a variety of cryptographic mechanisms that allow to perform some computation without directly obtaining knowledge of plaintext information. However, many challenges have so far prevented effective real-world usage in many existing applications. For one, some mechanisms leak some information or have been proposed outside of security models established within the cryptographic community, leaving open how effective they are at protecting privacy in various applications. Additionally, a major challenge causing PETs to remain largely academic is their practicality-in both efficiency and usability. Cryptographic mechanisms introduce a lot of overhead, which is mostly prohibitive, and due to a lack of high-level tools are very hard to integrate for outsiders. In this thesis, we move towards making PETs more effective and practical in protecting privacy in numerous applications. We take a two-sided approach of first analyzing the effective security (cryptanalysis) of candidate mechanisms and then building constructions and tools (cryptographic engineering) for practical use in specified emerging applications in the domain of machine learning crucial to modern use cases. In the process, we incorporate an interdisciplinary perspective for analyzing mechanisms and by collaboratively building privacy-preserving architectures with requirements from the application domains' experts. Cryptanalysis. While mechanisms like Homomorphic Encryption (HE) or Secure Multi-Party Computation (SMPC) provably leak no additional information, Encrypted Search Algorithms (ESAs) and Randomization-only Two-Party Computation (RoTPC) possess additional properties that require cryptanalysis to determine effective privacy protection. ESAs allow for search on encrypted data, an important functionality in many applications. Most efficient ESAs possess some form of well-defined information leakage, which is cryptanalyzed via a breadth of so-called leakage attacks proposed in the literature. However, it is difficult to assess their practical effectiveness given that previous evaluations were closed-source, used restricted data, and made assumptions about (among others) the query distribution because real-world query data is very hard to find. For these reasons, we re-implement known leakage attacks in an open-source framework and perform a systematic empirical re-evaluation of them using a variety of new data sources that, for the first time, contain real-world query data. We obtain many more complete and novel results where attacks work much better or much worse than what was expected based on previous evaluations. RoTPC mechanisms require cryptanalysis as they do not rely on established techniques and security models, instead obfuscating messages using only randomizations. A prominent protocol is a privacy-preserving scalar product protocol by Lu et al. (IEEE TPDS'13). We show that this protocol is formally insecure and that this translates to practical insecurity by presenting attacks that even allow to test for certain inputs, making the case for more scrutiny of RoTPC protocols used as PETs. This part of the thesis is based on the following two publications: [KKM+22] S. KAMARA, A. KATI, T. MOATAZ, T. SCHNEIDER, A. TREIBER, M. YONLI. “SoK: Cryptanalysis of Encrypted Search with LEAKER - A framework for LEakage AttacK Evaluation on Real-world data”. In: 7th IEEE European Symposium on Security and Privacy (EuroS&P’22). Full version: https://ia.cr/2021/1035. Code: https://encrypto.de/code/LEAKER. IEEE, 2022, pp. 90–108. Appendix A. [ST20] T. SCHNEIDER , A. TREIBER. “A Comment on Privacy-Preserving Scalar Product Protocols as proposed in “SPOC””. In: IEEE Transactions on Parallel and Distributed Systems (TPDS) 31.3 (2020). Full version: https://arxiv.org/abs/1906.04862. Code: https://encrypto.de/code/SPOCattack, pp. 543–546. CORE Rank A*. Appendix B. Cryptographic Engineering. Given the above results about cryptanalysis, we investigate using the leakage-free and provably-secure cryptographic mechanisms of HE and SMPC to protect privacy in machine learning applications. As much of the cryptographic community has focused on PETs for neural network applications, we focus on two other important applications and models: Speaker recognition and sum product networks. We particularly show the efficiency of our solutions in possible real-world scenarios and provide tools usable for non-domain experts. In speaker recognition, a user's voice data is matched with reference data stored at the service provider. Using HE and SMPC, we build the first privacy-preserving speaker recognition system that includes the state-of-the-art technique of cohort score normalization using cohort pruning via SMPC. Then, we build a privacy-preserving speaker recognition system relying solely on SMPC, which we show outperforms previous solutions based on HE by a factor of up to 4000x. We show that both our solutions comply with specific standards for biometric information protection and, thus, are effective and practical PETs for speaker recognition. Sum Product Networks (SPNs) are noteworthy probabilistic graphical models that-like neural networks-also need efficient methods for privacy-preserving inference as a PET. We present CryptoSPN, which uses SMPC for privacy-preserving inference of SPNs that (due to a combination of machine learning and cryptographic techniques and contrary to most works on neural networks) even hides the network structure. Our implementation is integrated into the prominent SPN framework SPFlow and evaluates medium-sized SPNs within seconds. This part of the thesis is based on the following three publications: [NPT+19] A. NAUTSCH, J. PATINO, A. TREIBER, T. STAFYLAKIS, P. MIZERA, M. TODISCO, T. SCHNEIDER, N. EVANS. Privacy-Preserving Speaker Recognition with Cohort Score Normalisation”. In: 20th Conference of the International Speech Communication Association (INTERSPEECH’19). Online: https://arxiv.org/abs/1907.03454. International Speech Communication Association (ISCA), 2019, pp. 2868–2872. CORE Rank A. Appendix C. [TNK+19] A. TREIBER, A. NAUTSCH , J. KOLBERG , T. SCHNEIDER , C. BUSCH. “Privacy-Preserving PLDA Speaker Verification using Outsourced Secure Computation”. In: Speech Communication 114 (2019). Online: https://encrypto.de/papers/TNKSB19.pdf. Code: https://encrypto.de/code/PrivateASV, pp. 60–71. CORE Rank B. Appendix D. [TMW+20] A. TREIBER , A. MOLINA , C. WEINERT , T. SCHNEIDER , K. KERSTING. “CryptoSPN: Privacy-preserving Sum-Product Network Inference”. In: 24th European Conference on Artificial Intelligence (ECAI’20). Full version: https://arxiv.org/abs/2002.00801. Code: https://encrypto.de/code/CryptoSPN. IOS Press, 2020, pp. 1946–1953. CORE Rank A. Appendix E. Overall, this thesis contributes to a broader security analysis of cryptographic mechanisms and new systems and tools to effectively protect privacy in various sought-after applications