Search Results for author:
Found 40 papers, 16 papers with code
Detecting Entailment in Code-Mixed Hindi-English Conversations
The presence of large-scale corpora for Natural Language Inference (NLI) has spurred deep learning research in this area, though much of this research has focused solely on monolingual data.
Phone Inventories and Recognition for Every Language
Identifying phone inventories is a crucial component in language documentation and the preservation of endangered languages.
Task-Specific Pre-Training and Cross Lingual Transfer for Sentiment Analysis in Dravidian Code-Switched Languages
Sentiment analysis in Code-Mixed languages has garnered a lot of attention in recent years.
SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in HuBERT
Data-driven unit discovery in self-supervised learning (SSL) of speech has embarked on a new era of spoken language processing.
Self-Supervised Models of Speech Infer Universal Articulatory Kinematics
Self-Supervised Learning (SSL) based models of speech have shown remarkable performance on a range of downstream tasks.
Deep Speech Synthesis from MRI-Based Articulatory Representations
Finally, through a series of ablations, we show that the proposed MRI representation is more comprehensive than EMA and identify the most suitable MRI feature subset for articulatory synthesis.
Speaker-Independent Acoustic-to-Articulatory Speech Inversion
To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interpretable space.
Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization
In this work, we propose a novel articulatory representation decomposition algorithm that takes the advantage of guided factor analysis to derive the articulatory-specific factors and factor scores.
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models
End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation.
A Fast and Accurate Pitch Estimation Algorithm Based on the Pseudo Wigner-Ville Distribution
Estimation of fundamental frequency (F0) in voiced segments of speech signals, also known as pitch tracking, plays a crucial role in pitch synchronous speech analysis, speech synthesis, and speech manipulation.
CTC Alignments Improve Autoregressive Translation
Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Deep Speech Synthesis from Articulatory Representations
In the articulatory synthesis task, speech is synthesized from input features containing information about the physical behavior of the human vocal tract.
ASR2K: Speech Recognition for Around 2000 Languages without Audio
We achieve 50% CER and 74% WER on the Wilderness dataset with Crubadan statistics only and improve them to 45% CER and 69% WER when using 10000 raw text utterances.
Building African Voices
This paper focuses on speech synthesis for low-resourced African languages, from corpus creation to sharing and deploying the Text-to-Speech (TTS) systems.
On Advances in Text Generation from Images Beyond Captioning: A Case Study in Self-Rationalization
Combining the visual modality with pretrained language models has been surprisingly effective for simple descriptive tasks such as image captioning.
Deep Neural Convolutive Matrix Factorization for Articulatory Representation Decomposition
Most of the research on data-driven speech representation learning has focused on raw audios in an end-to-end manner, paying little attention to their internal phonological or gestural structure.
ESPnet-SLU: Advancing Spoken Language Understanding through ESPnet
However, there are few open source toolkits that can be used to generate reproducible results on different Spoken Language Understanding (SLU) benchmarks.
Cross-lingual Transfer for Speech Processing using Acoustic Language Similarity
We demonstrate the effectiveness of our approach in language family classification, speech recognition, and speech synthesis tasks.
Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units
For the English language, these LMs treat words as atomic units, which presents inherent challenges to language modelling in the speech domain.
Intent Classification Using Pre-trained Language Agnostic Embeddings For Low Resource Languages
We perform experiments across three different languages: English, Sinhala, and Tamil each with different data sizes to simulate high, medium, and low resource scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Speech Summarization using Restricted Self-Attention
End-to-end modeling of speech summarization models is challenging due to memory and compute constraints arising from long input audio sequences.
Breaking Down Walls of Text: How Can NLP Benefit Consumer Privacy?
Privacy plays a crucial role in preserving democratic ideals and personal autonomy.
Rethinking End-to-End Evaluation of Decomposable Tasks: A Case Study on Spoken Language Understanding
Our splits identify performance gaps up to 10% between end-to-end systems that were within 1% of each other on the original test sets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
CodemixedNLP: An Extensible and Open NLP Toolkit for Code-Mixing
The NLP community has witnessed steep progress in a variety of tasks across the realms of monolingual and multilingual language processing recently.
Grounding 'Grounding' in NLP
And finally, (3) How to advance our current definition to bridge the gap with Cognitive Science?
Focused Attention Improves Document-Grounded Generation
Document grounded generation is the task of using the information provided in a document to improve text generation.
Intent Recognition and Unsupervised Slot Identification for Low Resourced Spoken Dialog Systems
We build a word-free natural language understanding module that does intent recognition and slot identification from these phonetic transcription.
Unsupervised Self-Training for Sentiment Analysis of Code-Switched Data
We propose a general framework called Unsupervised Self-Training and show its applications for the specific use case of sentiment analysis of code-switched data.
NoiseQA: Challenge Set Evaluation for User-Centric Question Answering
When Question-Answering (QA) systems are deployed in the real world, users query them through a variety of interfaces, such as speaking to voice assistants, typing questions into a search engine, or even translating questions to languages supported by the QA system.
Automatically Identifying Language Family from Acoustic Examples in Low Resource Scenarios
Existing multilingual speech NLP works focus on a relatively small subset of languages, and thus current linguistic understanding of languages predominantly stems from classical approaches.
Acoustics Based Intent Recognition Using Discovered Phonetic Units for Low Resource Languages
With the aim of aiding development of spoken dialog systems in low resourced languages, we propose a novel acoustics based intent recognition system that uses discovered phonetic units for intent classification.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Understanding Linguistic Accommodation in Code-Switched Human-Machine Dialogues
Code-switching is a ubiquitous phenomenon in multilingual communities.
Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages
In this work, we design a knowledge-base and prediction model embedded system for spelling correction in low-resource languages.
Positioning yourself in the maze of Neural Text Generation: A Task-Agnostic Survey
Neural text generation metamorphosed into several critical natural language applications ranging from text completion to free form narrative generation.
Case Study: Deontological Ethics in NLP
Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices.
Mere account mein kitna balance hai? -- On building voice enabled Banking Services for Multilingual Communities
Tremendous progress in speech and language processing has brought language technologies closer to daily human life.
Zero-shot Learning for Speech Recognition with Universal Phonetic Model
Our model is able to recognize unseen phonemes in the target language, if only a small text corpus is available.
