12 accepted papers
Speaker-Attributed Automatic Speech Recognition (SAA) enhances traditional ASR systems by incorporating relative speaker identity tags directly into the transcript (e.g., [Speaker 1]:, [Speaker 2]:). In this work, we extend the capabilities of Granite-speech, a state-of-the-art speech-aware Large La…
In this paper, we take a step towards jointly modeling automatic speech recognition (STT) and speech synthesis (TTS) in a fully non-autoregressive way. We develop a novel multimodal framework capable of handling the speech and text modalities as input either individually or together. The proposed mo…
Self-supervised pre-trained models have consistently delivered state-of-art results in the fields of natural language and speech processing. However, we argue that their merits for modeling Turn-Taking for spoken dialogue systems still need further investigation. Due to that, in this paper we intro-…
Large speech emotion recognition datasets are hard to obtain, and small datasets may contain biases. Deep-net-based classifiers, in turn, are prone to exploit those biases and find shortcuts such as speaker characteristics. These shortcuts usually harm a model’s ability to generalize. To address thi…
Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speech emotion recognition (SER) still need further investigation. In this paper we introduce a modular End-to-End (E2E) SER…
Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-supervised representation learning-based systems are not yet considered state-of-the-art.We propose leveraging recent advance…
Speaker change detection is often addressed as a key component in speaker diarization systems. In this work we focus on online speaker change detection as a standalone task which is required for online closed captioning of broadcast television. Contrary to related works, we do not operate on frame-l…
We address the problem of liveness detection in audiovisual recordings for preventing spoofing attacks in biometric authentication systems. We assume that liveness is detected from a recording of a speaker saying a predefined phrase and that another recording of the same phrase is a priori available…
We introduce a novel approach of addressing inter-dataset variability in the context of speaker recognition in a mismatched condition under the JHU-2013 domain adaptation challenge (DAC) framework. Previously, we took a subspace removal approach for inter-dataset variability compensation (IDVC) of w…
In this paper we report our work on the recently collected text dependent speaker recognition dataset named RedDots, with a focus on the common passphrase condition. We first investigate an out-of-the-box approach. We then report several strategies to train on RedDots itself using up to 40 speakers…
In this paper we present a design of a DNN-based autoencoder for speech enhancement and its use for speaker recognition systems for distant microphones and noisy data. We started with augmenting the Fisher database with artificially noised and reverberated data and trained the autoencoder to map noi…
Nowadays state-of-the-art speaker recognition systems obtain quite accurate results for both text-independent and text-dependent tasks as long as they are trained on a fair amount of development data from the target domain, and as long as the target data is clean. In this work we investigate the use…