Héctor Delgado

Nuance Communications

Recent evaluations such as ASVspoof 2015 and the similarly-named AVspoof have stimulated a great deal of progress to develop spoofing countermeasures for automatic speaker verification. This paper reports an approach which combines speech signal analysis using the constant Q transform with traditional cepstral processing. The resulting constant Q c...

Concerns regarding the vulnerability of automatic speaker verification (ASV) technology against spoofing can undermine confidence in its reliability and form a barrier to exploitation. The absence of competitive evaluations and the lack of common datasets has hampered progress in developing effective spoofing countermeasures. This paper describes t...

This paper describes the speaker diarization system submitted by EURECOM for the Albayzin 2016 speaker diarization evaluation. This evaluation consists of segmenting broadcast audio documents according to different speakers and attributing those segments to the speaker who uttered them, without any prior information about the speaker identities nor...

Efforts to develop new countermeasures in order to protect automatic speaker verification from spoofing have intensified over recent years. The ASVspoof 2015 initiative showed that there is great potential to detect spoofing attacks, but also that the detection of previously unforeseen spoofing attacks remains challenging. This paper argues that th...

Speaker diarization has become a key process within other speech processing systems which take advantage of single-speaker speech signals. Furthermore, finding recurrent speakers among a set of audio recordings, known as cross-show diarization, is gaining attention in the last years. Current state-of-the-art-systems provide good performance, but us...

We present Malacopula, a neural-based generalised Hammerstein model designed to introduce adversarial perturbations to spoofed speech utterances so that they better deceive automatic speaker verification (ASV) systems. Using non-linear processes to modify speech utterances, Malacopula enhances the effectiveness of spoofing attacks. The model compri...

ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions. Compared to previous challenges, the ASVspoof 5 database is built from crowdsourced data collected from a vastly greater number of speakers in diverse acoustic conditions. Attacks, also cro...

Over the past few years, significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers. The use of standard databases and evalu...

Benchmarkinginitiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typically reflect a progressive evolution from ideal lab conditions towards to those encountered in the wild. ASVspoof, the spoofing and deepfake detection initiative and chall...

Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typically reflect a progressive evolution from ideal lab conditions towards to those encountered in the wild. ASVspoof, the spoofing and deepfake detection initiative and chal...

Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent, they have typically evolved as standalone sub-systems whereby CM solutions are usually designed for a fixed ASV system. The work reported in this paper aims to gauge th...

For many decades, research in speech technologies has focused upon improving reliability. With this now meeting user expectations for a range of diverse applications, speech technology is today omni-present. As result, a focus on security and privacy has now come to the fore. Here, the research effort is in its relative infancy and progress calls f...

The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of spoofing and the development of countermeasures. ASVspoof 2021 is the 4th in a series of bi-annual, competitive challenges where the goal is to develop countermeasures capable of discri...

ASVspoof 2021 is the forth edition in the series of bi-annual challenges which aim to promote the study of spoofing and the design of countermeasures to protect automatic speaker verification systems from manipulation. In addition to a continued focus upon logical and physical access tasks in which there are a number of advances compared to previou...

Whether it be for results summarization, or the analysis of classifier fusion, some means to compare different classifiers can often provide illuminating insight into their behaviour, (dis)similarity or complementarity. We propose a simple method to derive 2D representation from detection scores produced by an arbitrary set of binary classifiers in...

The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third in a series of bi-annual challenges: ASVspoof 2019. With the challenge database and protocols being described elsewhere, the focus of this paper is on results and the top performing single and ensembl...

The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV). This paper describes the third in a series of bi-annual challenges: ASVspoof 2019. With the challenge database and protocols being described elsewhere, the focus of this paper is on results and the top performing single and ensembl...

Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs. The reliability of spoofing CMs is typically gauged using the equal error rate (EER) metric. The primitive EER fails to reflect application requirements and...

Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs. The reliability of spoofing CMs is typically gauged using the equal error rate (EER) metric. The primitive EER fails to reflect application requirements and...

Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, also referred to as “presentation attacks.” These vulnerabilities are generally unacceptable and call for spoofing countermeasures or “presentation...

Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, also referred to as ''presentation attacks.'' These vulnerabilities are generally unacceptable and call for spoofing countermeasures or "presentati...

Speech recordings are a rich source of personal, sensitive data that can be used to support a plethora of diverse applications,from health profiling to biometric recognition. It is therefore essential that speech recordings are adequately protected so that they cannot be misused. Such protection, in the form of privacy-preserving technologies, is...

The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE). The latest edition of such joint submission was in SRE 2018, in which the I4U submission was among the best-performing systems. SRE'18 also marks the 10-year anniversary of I4U consortium into NIST SRE series of evaluation. The primary obje...

ASVspoof, now in its third edition, is a series of community-led challenges which promote the development of countermeasures to protect automatic speaker verification (ASV) from the threat of spoofing. Advances in the 2019 edition include: (i) a consideration of both logical access (LA) and physical access (PA) scenarios and the three major forms o...

Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers. The use of standard databases and evalua...

Over the past few years, significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers. The use of standard databases and evalu...

Low-latency speaker spotting (LLSS) calls for the rapid detection of known speakers within multi-speaker audio streams. While previous work showed the potential to develop efficient LLSS solutions by combining speaker diarization and speaker detection within an online processing framework, it failed to move significantly beyond the traditional defi...

The assessment of performance for any number of speech processing tasks calls for the use of a suitably large, representative dataset. Dataset design is crucial so as to ensure that any significant variation unrelated to the task in hand is adequately normalised or marginalised. Most datasets are partitioned into training, development and evaluatio...

The vulnerability of automatic speaker verification (ASV) systems to spoofing is widely acknowledged. Recent years have seen an intensification in research efforts to develop spoofing countermeasures, also known as presentation attack detection (PAD) systems. Much of this work has involved the exploration of features that discriminate reliably betw...

The first DIHARD challenge aims to promote speaker diariza-tion research and to foster progress in domain robustness. This paper reports EURECOM's submission to the DIHARD challenge. It is based upon a low-resource, domain-robust binary key approach to speaker modelling. New contributions include the use of an infinite impulse response-constant Q M...

The now-acknowledged vulnerabilities of automatic speaker verification (ASV) technology to spoofing attacks have spawned interests to develop so-called spoofing countermeasures. By providing common databases, protocols and metrics for their assessment, the ASVspoof initiative was born to spear-head research in this area. The first competitive ASVsp...

This paper introduces a new task termed low-latency speaker spotting (LLSS). Related to security and intelligence applications , the task involves the detection, as soon as possible, of known speakers within multi-speaker audio streams. The paper describes differences to the established fields of speaker diariza-tion and automatic speaker verificat...

The ASVspoof challenge series was born to spearhead research in anti-spoofing for automatic speaker verification (ASV). The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric. While a strategic approach to assessment at the time, it has certa...

The ASVspoof challenge series was born to spearhead research in anti-spoofing for automatic speaker verification (ASV). The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric. While a strategic approach to assessment at the time, it has certa...

The subject of biometrics spoofing has received more attention over the last few years within the research community. For speech technology spoofing attacks [1] relate to replay attacks [2, 6] and a category sometimes referred to as synthetic voice attacks, including attacks with artificial signals such as voice conversion and speech synthesis [3,...

Speaker change detection can be of benefit to a number of different speech processing tasks such as speaker diarization, recognition and detection. Current solutions rely either on highly localized data or on training with large quantities of background data. While efficient, the former tend to over-segment. While more stable, the latter are less e...

Vulnerabilities to presentation attacks can undermine confidence in automatic speaker verification (ASV) technology. While efforts to develop countermeasures, known as presentation attack detection (PAD) systems, are now under way, the majority of past work has been performed with high-quality speech data. Many practical ASV applications are narrow...

The 2016 speaker recognition evaluation (SRE'16) is the latest edition in the series of benchmarking events conducted by the National Institute of Standards and Technology (NIST). I4U is a joint entry to SRE'16 as the result from the collaboration and active exchange of information among researchers from sixteen Institutes and Universities across 4...

This paper describes a new database for the assessment of automatic speaker verification (ASV) vulnerabilities to spoofing attacks. In contrast to other recent data collection efforts, the new database has been designed to support the development of replay spoofing countermeasures tailored towards the protection of text-dependent ASV systems from r...

Many authentication applications involving automatic speaker verification (ASV) demand robust performance using short-duration, fixed or prompted text utterances. Text constraints not only reduce the phone-mismatch between enrolment and test utterances, which generally leads to improved performance, but also provide an ancillary level of security....

This paper introduces a new articulation rate filter and reports its combination with recently proposed constant Q cepstral coefficients (CQCCs) in their first application to automatic speaker verification (ASV). CQCC features are extracted with the constant Q transform (CQT), a perceptually-inspired alternative to Fourier-based approaches to time-...

Audio segmentation is important as a pre-processing task to improve the performance of many speech technology tasks and, therefore, it has an undoubted research interest. This paper describes the database, the metric, the systems and the results for the Albayzín-2014 audio segmentation campaign. In contrast to previous evaluations where the task wa...

Speaker diarization has become an important building block in many speech-related systems. Given the great increase of audiovisual media, fast systems are required in order to process large amounts of data in a reasonable time. In this regard, the recently proposed speaker diarization system based on binary key speaker modeling provides a very fast...

The recently proposed speaker diarization technique based on binary keys provides a very fast alternative to state-of-the-art systems. However, this speed up has the cost of a little increase in Diarization Error Rate (DER). This paper proposes a series of improvements to the original algorithm with the aim to get closer to state-of-the-art perform...

This article presents an overview of the technological components used in the process of audio description, and suggests a new scenario in which speech recognition, machine translation, and text-to-speech, with the corresponding human revision, could be used to increase audio description provision. The article focuses on a process in which both spe...

This paper describes the audio segmentation system developed by Transmedia Catalonia / Telecommunication and Systems Engineering Department, at the Autonomous University of Barcelona (UAB), for the Albayzin 2014 Audio Segmentation Evaluation. The evaluation task consists in segmenting spoken audio documents into three different acoustic classes (sp...

The recently proposed speaker diarization technique based on binary keys provides a very fast alternative to state-of-the-art systems with little increase of Diarization Error Rate (DER). Although the approach shows great potential, it also presents issues, mainly in the stopping criterion. Therefore, exploring alternative clustering/stopping crite...

Speaker diarization is the task of partitioning an audio stream into homogeneous segments according to speaker identity. Today state-of-the-art speaker diarization systems have achieved very competitive performance. However, any small improvement in Diarization Error Rate (DER) is usually subject to very large processing times (real time factor abo...

Ambient Assisted Living Technologies are providing sustainable and affordable solutions for the independent living of senior citizens. In this scenario, telemedicine systems enhance distance patient's health care through interactive audiovisual media at home. Today TV is becoming the main connected device at home. However, Interactive TV applicatio...

Today information extraction plays a significant role in management of massive data quantities for different purposes. One of the open challenges in this field is the automatic extraction of information from audio streams. This paper describes a useful metadata extraction system which performs a powerful combination of speech and speaker recognitio...

This paper describes the audio segmentation system developed at the Software-Hardware Prototypes and Solutions Lab (Autonomous University of Barcelona) for the Albayzin 2010 Evaluations at the FALA 2010 "VI Jornadas en Tecnología del Habla" and II Iberian SLTech Workshop.