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Found 33 papers, 1 papers with code
Impact of Microphone position Measurement Error on Multi Channel Distant Speech Recognition & Intelligibility
It was shown in (Raikar et al., 2020) that the measurement error in the microphone position affected the room impulse response (RIR) which in turn affected the single channel speech recognition.
A Novel Scheme to classify Read and Spontaneous Speech
The COVID-19 pandemic has led to an increased use of remote telephonic interviews, making it important to distinguish between scripted and spontaneous speech in audio recordings.
Selective Data Augmentation for Robust Speech Translation
Though resource intensive, e2e-ST systems have the inherent ability to retain para and non-linguistic characteristics of the speech unlike cascade systems.
Text-to-Audio Grounding Based Novel Metric for Evaluating Audio Caption Similarity
Automatic Audio Captioning (AAC) refers to the task of translating an audio sample into a natural language (NL) text that describes the audio events, source of the events and their relationships.
Computing Optimal Location of Microphone for Improved Speech Recognition
It was shown in our earlier work that the measurement error in the microphone position affected the room impulse response (RIR) which in turn affected the single-channel close microphone and multi-channel distant microphone speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Spectro Temporal EEG Biomarkers For Binary Emotion Classification
The usefulness of these features for EEG emotion classification is investigated through extensive experiments using state-of-the-art classifiers.
Automatic Audio Captioning using Attention weighted Event based Embeddings
Automatic Audio Captioning (AAC) refers to the task of translating audio into a natural language that describes the audio events, source of the events and their relationships.
Synthetic speech detection using meta-learning with prototypical loss
Furthermore, the proposed system with data augmentation outperforms the ASVspoof 2021 challenge best baseline both in the progress and evaluation phase of the LA task.
A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios
In this paper, we propose a novel three step technique to address imbalanced data.
Automatic Speaker Independent Dysarthric Speech Intelligibility Assessment System
Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery.
Semi Supervised Learning For Few-shot Audio Classification By Episodic Triplet Mining
In this paper, we propose to replace the typical prototypical loss function with an Episodic Triplet Mining (ETM) technique.
Identification of Dementia Using Audio Biomarkers
The objective of this work is to use speech processing and machine learning techniques to automatically identify the stage of dementia such as mild cognitive impairment (MCI) or Alzheimers disease (AD).
A Cycle-GAN Approach to Model Natural Perturbations in Speech for ASR Applications
Naturally introduced perturbations in audio signal, caused by emotional and physical states of the speaker, can significantly degrade the performance of Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Deep Learning approach for Hindi Named Entity Recognition
Named Entity Recognition is one of the most important text processing requirement in many NLP tasks.
Sentiment Analysis using Imperfect Views from Spoken Language and Acoustic Modalities
Multimodal sentiment classification in practical applications may have to rely on erroneous and imperfect views, namely (a) language transcription from a speech recognizer and (b) under-performing acoustic views.
Automatic Speech Recognition (ASR)
General Classification
+2
A Novel Approach for Effective Learning in Low Resourced Scenarios
Deep learning based discriminative methods, being the state-of-the-art machine learning techniques, are ill-suited for learning from lower amounts of data.
Adapting general-purpose speech recognition engine output for domain-specific natural language question answering
However, when such engines are used for specific domains, they may not recognize domain-specific words well, and may produce erroneous output.
k-FFNN: A priori knowledge infused Feed-forward Neural Networks
It is not immediately clear (a) how a priori temporal knowledge can be used in a FFNN architecture (b) how a FFNN performs when provided with this knowledge about temporal correlations (assuming available) during training.
Evaluating the Performance of a Speech Recognition based System
The usual mechanism to evaluate the performance of a speech solution is to do an extensive test of the system by putting it to actual people use and then evaluating the performance by analyzing the logs for successful transactions.
Mobile Phone Based Vehicle License Plate Recognition for Road Policing
Identity of a vehicle is done through the vehicle license plate by traffic police in general.
On-line Handwritten Devanagari Character Recognition using Fuzzy Directional Features
This paper describes a new feature set for use in the recognition of on-line handwritten Devanagari script based on Fuzzy Directional Features.
Voice based self help System: User Experience Vs Accuracy
A speech based self help system ideally needs a speech recognition engine to convert spoken speech to text and in addition a language processing engine to take care of any misrecognitions by the speech recognition engine.
A Rule-Based Short Query Intent Identification System
Using SMS (Short Message System), cell phones can be used to query for information about various topics.
Online Handwritten Devanagari Stroke Recognition Using Extended Directional Features
Experimental results show that the extended directional feature set performs well with about 65+% stroke level recognition accuracy for writer independent data set.
Modified Mel Filter Bank to Compute MFCC of Subsampled Speech
Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in most speech and speaker recognition applications.
A Framework for On-Line Devanagari Handwritten Character Recognition
The framework is based on identify- ing strokes, which in turn lead to recognition of handwritten on-line characters rather that the conventional character identification.
Choice of Mel Filter Bank in Computing MFCC of a Resampled Speech
Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in most speech and speaker recognition applications.
Music and Vocal Separation Using Multi-Band Modulation Based Features
We first identify the distribution of these non-linear features for music only and voice only segments in the audio signal in different Mel spaced frequency bands and show that they have the ability to discriminate.
Basis Identification for Automatic Creation of Pronunciation Lexicon for Proper Names
The idea is to construct a small orthogonal set of words (basis) which can span the set of names in a given database.
Automatic Segmentation of Broadcast News Audio using Self Similarity Matrix
Generally audio news broadcast on radio is com- posed of music, commercials, news from correspondents and recorded statements in addition to the actual news read by the newsreader.
