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Found 20 papers, 11 papers with code
TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenes
However, the exploration of 3D dense captioning in outdoor scenes is hindered by two major challenges: 1) the \textbf{domain gap} between indoor and outdoor scenes, such as dynamics and sparse visual inputs, makes it difficult to directly adapt existing indoor methods; 2) the \textbf{lack of data} with comprehensive box-caption pair annotations specifically tailored for outdoor scenes.
Large Language Models Powered Context-aware Motion Prediction
Traditional methods of motion forecasting primarily encode vector information of maps and historical trajectory data of traffic participants, lacking a comprehensive understanding of overall traffic semantics, which in turn affects the performance of prediction tasks.
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models
Recently, instruction-following audio-language models have received broad attention for audio interaction with humans.
Ranked #1 on
Acoustic Scene Classification
on TUT Acoustic Scenes 2017
(using extra training data)
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
In this paper, we propose LauraGPT, a unified GPT model for audio recognition, understanding, and generation.
Accurate and Reliable Confidence Estimation Based on Non-Autoregressive End-to-End Speech Recognition System
Estimating confidence scores for recognition results is a classic task in ASR field and of vital importance for kinds of downstream tasks and training strategies.
Overview of the ICASSP 2023 General Meeting Understanding and Generation Challenge (MUG)
ICASSP2023 General Meeting Understanding and Generation Challenge (MUG) focuses on prompting a wide range of spoken language processing (SLP) research on meeting transcripts, as SLP applications are critical to improve users' efficiency in grasping important information in meetings.
MUG: A General Meeting Understanding and Generation Benchmark
To prompt SLP advancement, we establish a large-scale general Meeting Understanding and Generation Benchmark (MUG) to benchmark the performance of a wide range of SLP tasks, including topic segmentation, topic-level and session-level extractive summarization and topic title generation, keyphrase extraction, and action item detection.
Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model
Conventional ASR systems use frame-level phoneme posterior to conduct force-alignment~(FA) and provide timestamps, while end-to-end ASR systems especially AED based ones are short of such ability.
INT2: Interactive Trajectory Prediction at Intersections
One of the most challenging problems in motion forecasting is interactive trajectory prediction, whose goal is to jointly forecasts the future trajectories of interacting agents.
MMSpeech: Multi-modal Multi-task Encoder-Decoder Pre-training for Speech Recognition
Therefore, we propose to introduce the phoneme modality into pre-training, which can help capture modality-invariant information between Mandarin speech and text.
Ranked #2 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition
However, due to an independence assumption within the output tokens, performance of single-step NAR is inferior to that of AR models, especially with a large-scale corpus.
Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios
Through this formulation, we propose the speaker embedding-aware neural diarization (SEND) framework, where a speech encoder, a speaker encoder, two similarity scorers, and a post-processing network are jointly optimized to predict the encoded labels according to the similarities between speech features and speaker embeddings.
Ranked #1 on
Speaker Diarization
on AliMeeting
ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech
Specifically, we first introduce a word-level prosody encoder, which quantizes the low-frequency band of the speech and compresses prosody attributes in the latent prosody vector (LPV).
BeamTransformer: Microphone Array-based Overlapping Speech Detection
We propose BeamTransformer, an efficient architecture to leverage beamformer's edge in spatial filtering and transformer's capability in context sequence modeling.
A Real-time Speaker Diarization System Based on Spatial Spectrum
In this paper we describe a speaker diarization system that enables localization and identification of all speakers present in a conversation or meeting.
Streaming Chunk-Aware Multihead Attention for Online End-to-End Speech Recognition
Recently, streaming end-to-end automatic speech recognition (E2E-ASR) has gained more and more attention.
Sound Audio and Speech Processing
Neural Zero-Inflated Quality Estimation Model For Automatic Speech Recognition System
The performances of automatic speech recognition (ASR) systems are usually evaluated by the metric word error rate (WER) when the manually transcribed data are provided, which are, however, expensively available in the real scenario.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Automatic Spelling Correction with Transformer for CTC-based End-to-End Speech Recognition
Results in a 20, 000 hours Mandarin speech recognition task show that the proposed spelling correction model can achieve a CER of 3. 41%, which results in 22. 9% and 53. 2% relative improvement compared to the baseline CTC-based systems decoded with and without language model respectively.
Deep-FSMN for Large Vocabulary Continuous Speech Recognition
In a 20000 hours Mandarin recognition task, the LFR trained DFSMN can achieve more than 20% relative improvement compared to the LFR trained BLSTM.
Deep Feed-forward Sequential Memory Networks for Speech Synthesis
The Bidirectional LSTM (BLSTM) RNN based speech synthesis system is among the best parametric Text-to-Speech (TTS) systems in terms of the naturalness of generated speech, especially the naturalness in prosody.
