A laryngectomee is a person whose larynx has been removed by surgery, usually due to laryngeal cancer. After surgery, most laryngectomees are able to speak again, using techniques that are learned with the help of a speech therapist. This is termed as alaryngeal speech, and esophageal speech (ES) is one of the several alaryngeal speech production modes. A considerable amount of research has been dedicated to the study of alaryngeal speech, with a wide range of aims such as helping speech therapists with evaluation and diagnosis, and improving its quality and intelligibility using digital signal processing techniques. We present to you a database of Spanish ES voices, named AhoSLABI, which is designed to allow the development of new support technologies for this speech impairment. The database primarily consists of recordings of 31 laryngectomees (27 males and 4 females) pronouncing phonetically balanced sentences. Additionally, it includes parallel recordings of the sentences by 9 healthy speakers (6 males and 3 females) to facilitate speech processing tasks that require small parallel corpora, such as voice conversion or synthetic speech adaptation. Apart from the sentences, the database includes sustained vowels and a small set of isolated words, which can be valuable for research on ES analysis, diagnosis and evaluation. The paper describes the main contents of the database, the recording protocols and procedure, as well as the labeling process. The main acoustic characteristics of the voices, such as speaking rate, durations of the recordings, phones and silences, and other such characteristics are compared with those of a reduced set of healthy voices. In addition, we describe an experiment using the database to improve the performance of an ASR system for ES speakers. This new resource will be made available to the scientific community with the hope that it will be used to improve the quality of life of the laryngectomees.

This paper introduces the Synthetic Speech Detection system developed by Aholab for the Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof 2015). The detector is a classifier based on Gaussian Mixture Models that are created using the Relative Phase Shift (RPS) transformation for the phase information. Different strategies have been evaluated: modeling the specific attacks using the information provided by the ASVspoof 2015 organizers, and modeling the vocoders possibly used in the spoofing signals, using data from previous works. The evaluation results show that attack specific models work for known attacks but they do not cope with the unknown attacks correctly. When using vocoder models build with other databases, the results suggest that the followed strategy do not take advantage of the available data and thus model adaptation should be explored. Index Terms: synthetic speech detection, phase information, anti-spoofing

In the field of speaker verification (SV) it is nowadays feasible and relatively easy to create a synthetic voice to deceive a speech driven biometric access system. This paper presents a synthetic speech detector that can be connected at the front-end or at the back-end of a standard SV system, and that will protect it from spoofing attacks coming from state-of-the-art statistical Text to Speech (TTS) systems. The system described is a Gaussian Mixture Model (GMM) based binary classifier that uses natural and copy-synthesized signals obtained from the Wall Street Journal database to train the system models. Three different state-of-the-art vocoders are chosen and modeled using two sets of acoustic parameters: 1) relative phase shift and 2) canonical Mel Frequency Cepstral Coefficients (MFCC) parameters, as baseline. The vocoder dependency of the system and multivocoder modeling features are thoroughly studied. Additional phase-aware vocoders are also tested. Several experiments are carried out, showing that the phase-based parameters perform better and are able to cope with new unknown attacks. The final evaluations, testing synthetic TTS signals obtained from the Blizzard challenge, validate our proposal.

The importance of phase information in the perceptual quality of the speech signals is studied in this paper. Many speech synthesisers do not use the original phase information of the signals assuming their contribution is almost inaudible. The Relative Phase Shift (RPS) representation of the phase allows straightforward phase structure analysis, manipulation and resynthesis, and we use these features to do a comparative evaluation of some phase modifications usually found in speech models. The final intention of this study is to get an answer to the question of whether phases deserve elaborate models to get high quality synthetic speech, or their subtle effects justify overlooking them. Index Terms: phase perception, RPS, speech synthesis. 1.

In this paper, we evaluate the vulnerability of speaker verification (SV) systems to synthetic speech. The SV systems are based on either the Gaussian mixture model–universal background model (GMM-UBM) or support vector machine (SVM) using GMM supervectors. We use a hidden Markov model (HMM)-based text-to-speech (TTS) synthesizer, which can synthesize speech for a target speaker using small amounts of training data through model adaptation of an average voice or background model. Although the SV systems have a very low equal error rate (EER), when tested with synthetic speech generated from speaker models derived from the Wall Street Journal (WSJ) speech corpus, over 81% of the matched claims are accepted. This result suggests vulnerability in SV systems and thus a need to accurately detect synthetic speech. We propose a new feature based on relative phase shift (RPS), demonstrate reliable detection of synthetic speech, and show how this classifier can be used to improve security of SV systems.

In this paper, we present new results from our research into the vulnerability of a speaker verification (SV) system to synthetic speech. We use a HMM-based speech synthesizer, which creates synthetic speech for a targeted speaker through adaptation of a background model and both GMM-UBM and support vector machine (SVM) SV systems. Using 283 speakers from the Wall-Street Journal (WSJ) corpus, our SV systems have a 0.35% EER. When the systems are tested with synthetic speech generated from speaker models derived from the WSJ journal corpus, over 91% of the matched claims are accepted. We propose the use of relative phase shift (RPS) in order to detect synthetic speech and develop a GMM-based synthetic speech classifier (SSC). Using the SSC, we are able to correctly classify human speech in 95% of tests and synthetic speech in 88% of tests thus significantly reducing the vulnerability.

Voice conversion has been traditionally focused on spectrum. Current systems lack a solid prosody conversion method suitable for different speaking styles. Recently, the unit selection technique has been applied to transform emotional intonation contours. This paper goes one step beyond: it explores strategies for training and configuring the selection cost function in an emotion conversion application. The proposed system, which uses accent groups as basic intonation units and performs conversion also on phoneme durations and intensity, is evaluated by means of a carefully designed subjective test involving the big six emotions. Although the expressiveness of the converted sentences is still far from that of natural emotional speech, satisfactory results are obtained when different configurations are used for different emotions.

A novel algorithm based on classical cepstrum calculation followed by dynamic programming is presented in this paper. The algorithm has been evaluated with a 60-minutes database containing 60 speakers and different recording conditions and environments. A second reference database has also been used. In addition, the performance of four popular PDA algorithms has been evaluated with the same databases. The results prove the good performance of the described algorithm in noisy conditions. Furthermore, the paper is a first initiative to perform an evaluation of widely used PDA algorithms over an extensive and realistic database.