18 accepted papers
Speakers change vocal intensity in daily life to communicate over long distances and to express vocal emotions. Humans produce speech using different intensity categories (e.g. soft, normal and loud voice) and they can regulate intensity across a wide sound pressure level (SPL) range. Knowing the in…
Regulation of vocal intensity is a fundamental phenomenon in speech communication. Vocal intensity can be quantified using sound pressure level (SPL), which can be measured easily by recording a standard calibration signal with speech and by comparing the energy of the recorded speech signal with th…
Automatic detection of voice disorders from acoustic speech signals can help to improve reliability of medical diagnosis. However, the real-life environment in which speech signals are recorded for diagnosis can be different from the environment in which the detection system’s training data was orig…
Automatic detection and severity level classification of dysarthria directly from acoustic speech signals can be used as a tool in medical diagnosis. In this work, the pre-trained wav2vec 2.0 model is studied as a feature extractor to build detection and severity level classification systems for dys…
In production of voiced speech, epochs or glottal closure instants (GCIs) refer to the instants of significant excitation of the vocal tract. Extraction of GCIs is used as a pre-processing stage in many areas of speech technology, such as in prosody modification, speech synthesis and voice source an…
The performance of automatic speech recognition systems for children’s speech is known to suffer from the large variation and mismatch in the acoustic and linguistic attributes between children’s and adults’ speech. One of the various identified sources of mismatch is the difference in formant frequ…
Speaking style conversion (SSC) is the technology of converting natural speech signals from one style to another. In this study, we propose the use of cycle-consistent adversarial networks (CycleGANs) for converting styles with varying vocal effort, and focus on conversion between normal and Lombard…
This study explores various speech data augmentation methods for the task of noise-robust fundamental frequency (F0) estimation with neural networks. The explored augmentation strategies are split into additive noise and channel-based augmentation and into vocoder-based augmentation methods. In voco…
The state-of-the-art in text-to-speech (TTS) synthesis has recently improved considerably due to novel neural waveform generation methods, such as WaveNet. However, these methods suffer from their slow sequential inference process, while their parallel versions are difficult to train and even more c…
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis. First, we predict fundamental frequency and voicing information from M…
Auto-regressive modeling is a prevalent source-filter separation method of speech. Conventional linear prediction (LP) and its derivatives such as weighted linear prediction (WeLP) produce parametric spectral models within a linear frequency scale, whereas frequency-warped linear prediction (WaLP) c…
In statistical parametric speech synthesis (SPSS), a few studies have investigated the Lombard effect, specifically by using hidden Markov model (HMM)-based systems. Recently, artificial neural networks have demonstrated promising results in SPSS, specifically by using long short-term memory recurre…
Text-independent speaker verification (recognizing speakers regardless of content) and non-parallel voice conversion (transforming voice identities without requiring content-matched training utterances) are related problems. We adopt i-vector method to voice conversion. An i-vector is a fixed-dimens…
Speaker recognition performance degrades substantially in case of vocal effort mismatch (e.g. shouted vs. normal speech) between test and enrollment utterances. Such a mismatch is often encountered, for example, in forensic speaker recognition. This paper introduces a novel spectral mapping method w…
In studies on artificial bandwidth extension (ABE), there is a lack of international coordination in subjective tests between multiple methods and languages. Here we present the design of absolute category rating listening tests evaluating 12 ABE variants of six approaches in multiple languages, nam…
Achieving high quality and naturalness in statistical parametric synthesis of female voices remains to be difficult despite recent advances in the study area. Vocoding is one such key element in all statistical speech synthesizers that is known to affect the synthesis quality and naturalness. The pr…
Recent research on temporally weighted linear prediction shows that quasi closed phase (QCP) analysis of speech signals provides better modeling of the vocal tract and the glottal source. Quasi closed phase analysis gives more weightage on the closed phase of the glottal cycle, at the same time deem…
In the analysis of speech production, information about the voice source can be obtained non-invasively with glottal inverse filtering (GIF) methods. Current state-of-the-art GIF methods are capable of producing high-quality estimates in suitable conditions (e.g. low noise and reverberation), but th…