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Found 9 papers, 5 papers with code
Findings of the 2024 Mandarin Stuttering Event Detection and Automatic Speech Recognition Challenge
The StutteringSpeech Challenge focuses on advancing speech technologies for people who stutter, specifically targeting Stuttering Event Detection (SED) and Automatic Speech Recognition (ASR) in Mandarin.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection
The rapid advancements in speech technologies over the past two decades have led to human-level performance in tasks like automatic speech recognition (ASR) for fluent speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Spatial Processing Front-End For Distant ASR Exploiting Self-Attention Channel Combinator
We present a novel multi-channel front-end based on channel shortening with theWeighted Prediction Error (WPE) method followed by a fixed MVDR beamformer used in combination with a recently proposed self-attention-based channel combination (SACC) scheme, for tackling the distant ASR problem.
Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition
When a sufficiently large far-field training data is presented, jointly optimizing a multichannel frontend and an end-to-end (E2E) Automatic Speech Recognition (ASR) backend shows promising results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Simple Fusion of Deep and Shallow Learning for Acoustic Scene Classification
In this paper, we propose a system that consists of a simple fusion of two methods of the aforementioned types: a deep learning approach where log-scaled mel-spectrograms are input to a convolutional neural network, and a feature engineering approach, where a collection of hand-crafted features is input to a gradient boosting machine.
Towards an efficient deep learning model for musical onset detection
We first review the state-of-the-art deep learning models for MOD, and identify their shortcomings and challenges: (i) the lack of hyper-parameter tuning details, (ii) the non-availability of code for training models on other datasets, and (iii) ignoring the network capability when comparing different architectures.
Singing voice phoneme segmentation by hierarchically inferring syllable and phoneme onset positions
In the second step, the syllable and phoneme boundaries and labels are inferred hierarchically by using a duration-informed hidden Markov model (HMM).
Sound Information Retrieval Audio and Speech Processing
Audio to score matching by combining phonetic and duration information
We approach the singing phrase audio to score matching problem by using phonetic and duration information - with a focus on studying the jingju a cappella singing case.
Sound
Timbre Analysis of Music Audio Signals with Convolutional Neural Networks
The focus of this work is to study how to efficiently tailor Convolutional Neural Networks (CNNs) towards learning timbre representations from log-mel magnitude spectrograms.
Sound
