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Few Shot Speaker Recognition using Deep Neural NetworksApr 17 2019The recent advances in deep learning are mostly driven by availability of large amount of training data. However, availability of such data is not always possible for specific tasks such as speaker recognition where collection of large amount of data ... More Expediting TTS Synthesis with Adversarial VocodingApr 16 2019Recent approaches in text-to-speech (TTS) synthesis employ neural network strategies to vocode perceptually-informed spectrogram representations directly into listenable waveforms. Such vocoding procedures create a computational bottleneck in modern TTS ... More Audio-Visual Model Distillation Using Acoustic ImagesApr 16 2019In this paper, we investigate how to learn rich and robust feature representations for audio classification from visual data and a novel audio data modality, namely acoustic images. Former models learn audio representations from raw signals or spectral ... More Co-Separating Sounds of Visual ObjectsApr 16 2019Learning how objects sound from video is challenging, since they often heavily overlap in a single audio channel. Current methods for visually-guided audio source separation sidestep the issue by training with artificially mixed video clips, but this ... More Audio Denoising with Deep Network PriorsApr 16 2019We present a method for audio denoising that combines processing done in both the time domain and the time-frequency domain. Given a noisy audio clip, the method trains a deep neural network to fit this signal. Since the fitting is only partly successful ... More Spoof detection using x-vector and feature switchingApr 16 2019Detecting spoofed utterances is a fundamental problem in voice-based biometrics. Spoofing can be performed either by logical accesses like speech synthesis, voice conversion or by physical accesses such as replaying the pre-recorded utterance. Inspired ... More Semantic query-by-example speech search using visual groundingApr 15 2019A number of recent studies have started to investigate how speech systems can be trained on untranscribed speech by leveraging accompanying images at training time. Examples of tasks include keyword prediction and within- and across-mode retrieval. Here ... More Singing voice synthesis based on convolutional neural networksApr 15 2019The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing ... More Proximal binaural sound can induce subjective frissonApr 15 2019Sound frisson is a subjective experience wherein people tend to perceive the feeling of chills in addition to a physiological response, such as goosebumps. Multiple examples of frisson inducing sounds have been reported in the large online community, ... More SpeechYOLO: Detection and Localization of Speech ObjectsApr 14 2019In this paper, we propose to apply object detection methods from the vision domain on the speech recognition domain, by treating audio fragments as objects. More specifically, we present SpeechYOLO, which is inspired by the YOLO algorithm for object detection ... More Unsupervised Singing Voice ConversionApr 13 2019We present a deep learning method for singing voice conversion. The proposed network is not conditioned on the text or on the notes, and it directly converts the audio of one singer to the voice of another. Training is performed without any form of supervision: ... More Audio Compression Using Graph-based TransformApr 13 2019Graph-based Transform is one of the recent transform coding methods which has been used successfully in the state-of-art data decorrelation applications. In this paper, we propose a Graph-based Transform (GT) for audio compression. Hence, we introduce ... More Low-Latency Speaker-Independent Continuous Speech SeparationApr 13 2019Speaker independent continuous speech separation (SI-CSS) is a task of converting a continuous audio stream, which may contain overlapping voices of unknown speakers, into a fixed number of continuous signals each of which contains no overlapping speech ... More RNN-based speech synthesis using a continuous sinusoidal modelApr 12 2019Recently in statistical parametric speech synthesis, we proposed a continuous sinusoidal model (CSM) using continuous F0 (contF0) in combination with Maximum Voiced Frequency (MVF), which was successfully giving state-of-the-art vocoders performance (e.g. ... More The Sound of MotionsApr 11 2019Sounds originate from object motions and vibrations of surrounding air. Inspired by the fact that humans is capable of interpreting sound sources from how objects move visually, we propose a novel system that explicitly captures such motion cues for the ... More A Simple Baseline for Audio-Visual Scene-Aware DialogApr 11 2019The recently proposed audio-visual scene-aware dialog task paves the way to a more data-driven way of learning virtual assistants, smart speakers and car navigation systems. However, very little is known to date about how to effectively extract meaningful ... More STC Antispoofing Systems for the ASVspoof2019 ChallengeApr 11 2019This paper describes the Speech Technology Center (STC) antispoofing systems submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous challenges and includes 2 evaluation conditions: logical access use-case scenario ... More RawNet: Fast End-to-End Neural VocoderApr 10 2019Neural networks based vocoders have recently demonstrated the powerful ability to synthesize high quality speech. These models usually generate samples by conditioning on some spectrum features, such as Mel-spectrum. However, these features are extracted ... More A Framework for Multi-f0 Modeling in SATB Choir RecordingsApr 10 2019Fundamental frequency (f0) modeling is an important but relatively unexplored aspect of choir singing. Performance evaluation as well as auditory analysis of singing, whether individually or in a choir, often depend on extracting f0 contours for the singing ... More A New GAN-based End-to-End TTS Training AlgorithmApr 09 2019End-to-end, autoregressive model-based TTS has shown significant performance improvements over the conventional one. However, the autoregressive module training is affected by the exposure bias, or the mismatch between the different distributions of real ... More ASVspoof 2019: Future Horizons in Spoofed and Fake Audio DetectionApr 09 2019Apr 14 2019ASVspoof, 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 ... More Crossmodal Voice ConversionApr 09 2019Humans are able to imagine a person's voice from the person's appearance and imagine the person's appearance from his/her voice. In this paper, we make the first attempt to develop a method that can convert speech into a voice that matches an input face ... More Audio Classification of Bit-Representation WaveformApr 08 2019This paper investigates waveform representation for audio signal classification. Recently, many studies on audio waveform classification such as acoustic event detection and music genre classification have been increasing. Most studies on audio waveform ... More Duration robust sound event detectionApr 08 2019Task 4 of the Dcase2018 challenge demonstrated that substantially more research is needed for a real-world application of sound event detection. Analyzing the challenge results it can be seen that most successful models are biased towards predicting long ... More Direct Modelling of Speech Emotion from Raw SpeechApr 08 2019Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not ... More Direct Modelling of Speech Emotion from Raw SpeechApr 08 2019Apr 09 2019Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically crafted to echo human perception of speech signals. However, a filter bank that is designed from perceptual evidence is not ... More Improved Speaker-Dependent Separation for CHiME-5 ChallengeApr 08 2019This paper summarizes several follow-up contributions for improving our submitted NWPU speaker-dependent system for CHiME-5 challenge, which aims to solve the problem of multi-channel, highly-overlapped conversational speech recognition in a dinner party ... More Time Domain Audio Visual Speech SeparationApr 07 2019Audio-visual multi-modal modeling has been demonstrated to be effective in many speech related tasks, such as speech recognition and speech enhancement. This paper introduces a new time-domain audio-visual architecture for target speaker extraction from ... More VAE-based regularization for deep speaker embeddingApr 07 2019Deep speaker embedding has achieved state-of-the-art performance in speaker recognition. A potential problem of these embedded vectors (called `x-vectors') are not Gaussian, causing performance degradation with the famous PLDA back-end scoring. In this ... More VoiceID Loss: Speech Enhancement for Speaker VerificationApr 07 2019In this paper, we propose VoiceID loss, a novel loss function for training a speech enhancement model to improve the robustness of speaker verification. In contrast to the commonly used loss functions for speech enhancement such as the L2 loss, the VoiceID ... More Spoken Language Intent Detection using Confusion2VecApr 07 2019Decoding speaker's intent is a crucial part of spoken language understanding (SLU). The presence of noise or errors in the text transcriptions, in real life scenarios make the task more challenging. In this paper, we address the spoken language intent ... More Large Margin Softmax Loss for Speaker VerificationApr 06 2019In neural network based speaker verification, speaker embedding is expected to be discriminative between speakers while the intra-speaker distance should remain small. A variety of loss functions have been proposed to achieve this goal. In this paper, ... More Jasper: An End-to-End Convolutional Neural Acoustic ModelApr 05 2019In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data. Our model, Jasper, uses only 1D convolutions, batch normalization, ReLU, dropout, and residual connections. ... More