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Found 24 papers, 6 papers with code
On real-time multi-stage speech enhancement systems
We first provide a consolidated view of the roles of gain power factor, post-filter, and training labels for the Mel-scale masking model.
Cluster-based pruning techniques for audio data
In this work, we introduce, for the first time in the context of the audio domain, the k-means clustering as a method for efficient data pruning.
Multi-Channel MOSRA: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and a Teacher Model
Previous methods for predicting room acoustic parameters and speech quality metrics have focused on the single-channel case, where room acoustics and Mean Opinion Score (MOS) are predicted for a single recording device.
In-Ear-Voice: Towards Milli-Watt Audio Enhancement With Bone-Conduction Microphones for In-Ear Sensing Platforms
Such microphones can record the wearer's speech with much greater isolation, enabling personalized voice activity detection and further audio enhancement applications.
Speaker Embeddings as Individuality Proxy for Voice Stress Detection
However, the benchmark only evaluates performance separately on each dataset, but does not evaluate performance across the different types of stress and different languages.
ALO-VC: Any-to-any Low-latency One-shot Voice Conversion
This paper presents ALO-VC, a non-parallel low-latency one-shot phonetic posteriorgrams (PPGs) based voice conversion method.
BC-VAD: A Robust Bone Conduction Voice Activity Detection
Voice Activity Detection (VAD) is a fundamental module in many audio applications.
Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings
Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers.
BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping
Our results indicate that the hybrid model with a convolutional transformer as the encoder yields superior performance in most HEAR challenge tasks.
Ranked #1 on
Self-Supervised Learning
on CREMA-D
MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment
The acoustic environment can degrade speech quality during communication (e. g., video call, remote presentation, outside voice recording), and its impact is often unknown.
Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load
To that end, we introduce a set of five datasets for task load detection in speech.
AC-VC: Non-parallel Low Latency Phonetic Posteriorgrams Based Voice Conversion
This paper presents AC-VC (Almost Causal Voice Conversion), a phonetic posteriorgrams based voice conversion system that can perform any-to-many voice conversion while having only 57. 5 ms future look-ahead.
SERAB: A multi-lingual benchmark for speech emotion recognition
To facilitate the process, here, we present the Speech Emotion Recognition Adaptation Benchmark (SERAB), a framework for evaluating the performance and generalization capacity of different approaches for utterance-level SER.
PEAF: Learnable Power Efficient Analog Acoustic Features for Audio Recognition
At the end of Moore's law, new computing paradigms are required to prolong the battery life of wearable and IoT smart audio devices.
A Universal Deep Room Acoustics Estimator
Speech audio quality is subject to degradation caused by an acoustic environment and isotropic ambient and point noises.
Joint Blind Room Acoustic Characterization From Speech And Music Signals Using Convolutional Recurrent Neural Networks
Acoustic environment characterization opens doors for sound reproduction innovations, smart EQing, speech enhancement, hearing aids, and forensics.
Fast accuracy estimation of deep learning based multi-class musical source separation
Music source separation represents the task of extracting all the instruments from a given song.
FastVC: Fast Voice Conversion with non-parallel data
Despite the simple structure of the proposed model, it outperforms the VC Challenge 2020 baselines on the cross-lingual task in terms of naturalness.
Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing
Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+7
Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection
We describe an SNN training procedure that achieves low spiking activity and pruning algorithms to remove 85% of the network connections with no performance loss.
Deep speech inpainting of time-frequency masks
To address these limitations, here we propose an end-to-end framework for speech inpainting, the context-based retrieval of missing or severely distorted parts of time-frequency representation of speech.
Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding
Segmental errors are further propagated to optional suprasegmental (such as syllable) information coding.
Speech vocoding for laboratory phonology
Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal.
On Structured Sparsity of Phonological Posteriors for Linguistic Parsing
Building on findings from converging linguistic evidence on the gestural model of Articulatory Phonology as well as the neural basis of speech perception, we hypothesize that phonological posteriors convey properties of linguistic classes at multiple time scales, and this information is embedded in their support (index) of active coefficients.
