arXiv subjects

Qingliang Meng

Publications and source records attributed to Qingliang Meng.

5 recordsLinked to original sources

arXiv2025

FNH-TTS: Mixture-of-Experts Duration Modeling for Robust Neural Speech Synthesis

Natural and human-like speech depends on the coordination between prosodic timing and acoustic realization: duration modeling shapes rhythmic structure, while waveform generation determines whether that structure is rendered naturally. In natural speech, duration patterns vary across linguistic contexts and speakers, requiring a TTS system both to capture this variability and to faithfully realize it in the waveform. To address these challenges, we propose FNH-TTS, a VITS-based end-to-end system that jointly improves duration modeling and waveform generation. A mixture-of-experts duration predictor (MoE-DP) uses multiple experts and routing jointly conditioned on linguistic context and speaker information to model diverse duration patterns. For waveform generation, we adopt an inverse short-time Fourier transform (ISTFT)-based generator, providing a more direct and efficient synthesis path. We further employ multi-resolution and sub-band discriminators for fine-grained temporal and spectral adversarial supervision, thereby supporting natural waveform synthesis. Experiments on LJSpeech, VCTK, and LibriTTS show that FNH-TTS achieves the highest mean MOS on LJSpeech and VCTK and the highest duration-category accuracy on LibriTTS among the compared systems, together with competitive waveform reconstruction and substantially faster vocoder inference. Controlled analyses further show that MoE-DP primarily drives the duration-modeling gains, while the vocoder-side components make complementary contributions to synthesis quality and efficiency.

eess.AS↗

arXiv2025

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition

The deep integration of large language models and automatic speech recognition systems has become a promising research direction with high practical value. To address the overfitting issue commonly observed in Low-Rank Adaptation (LoRA) during the supervised fine-tuning (SFT) stage, this work proposes an innovative training paradigm Iterative LoRA Training (ILT) in combination with an Iterative Pseudo Labeling strategy, effectively enhancing the theoretical upper bound of model performance. Based on Whisper-large-v3 and Qwen2-Audio, we conduct systematic experiments using a three-stage training process: Focus Training, Feed Back Training, and Fix Training. Experimental results demonstrate the effectiveness of the proposed method. Furthermore, the MegaAIS research team applied this technique in the Interspeech 2025 Multilingual Conversational Speech Language Modeling Challenge (MLC-SLM), achieving 4th in Track 1 (Multilingual ASR Task) and 1st place in Track 2 (Speech Separation and Recognition Task), showcasing the practical feasibility and strong application potential of our approach.

cs.CL↗

arXiv2025

MTLM: Incorporating Bidirectional Text Information to Enhance Language Model Training in Speech Recognition Systems

Automatic speech recognition (ASR) systems normally consist of an acoustic model (AM) and a language model (LM). The acoustic model estimates the probability distribution of text given the input speech, while the language model calibrates this distribution toward a specific knowledge domain to produce the final transcription. Traditional ASR-specific LMs are typically trained in a unidirectional (left-to-right) manner to align with autoregressive decoding. However, this restricts the model from leveraging the right-side context during training, limiting its representational capacity. In this work, we propose MTLM, a novel training paradigm that unifies unidirectional and bidirectional manners through 3 training objectives: ULM, BMLM, and UMLM. This approach enhances the LM's ability to capture richer linguistic patterns from both left and right contexts while preserving compatibility with standard ASR autoregressive decoding methods. As a result, the MTLM model not only enhances the ASR system's performance but also support multiple decoding strategies, including shallow fusion, unidirectional/bidirectional n-best rescoring. Experiments on the LibriSpeech dataset show that MTLM consistently outperforms unidirectional training across multiple decoding strategies, highlighting its effectiveness and flexibility in ASR applications.

cs.CL↗

arXiv2025

Quantifying the Capability Boundary of DeepSeek Models: An Application-Driven Performance Analysis

DeepSeek-R1, known for its low training cost and exceptional reasoning capabilities, has achieved state-of-the-art performance on various benchmarks. However, detailed evaluations for DeepSeek Series models from the perspective of real-world applications are lacking, making it challenging for users to select the most suitable DeepSeek models for their specific needs. To address this gap, we presents the first comprehensive evaluation of the DeepSeek and its related models (including DeepSeek-V3, DeepSeek-R1, DeepSeek-R1-Distill-Qwen series, DeepSeek-R1-Distill-Llama series, their corresponding 4-bit quantized models, and the reasoning model QwQ-32B) using our enhanced A-Eval benchmark, A-Eval-2.0. Our systematic analysis reveals several key insights: (1) Given identical model architectures and training data, larger parameter models demonstrate superior performance, aligning with the scaling law. However, smaller models may achieve enhanced capabilities when employing optimized training strategies and higher-quality data; (2) Reasoning-enhanced model show significant performance gains in logical reasoning tasks but may underperform in text understanding and generation tasks; (3) As the data difficulty increases, distillation or reasoning enhancements yield higher performance gains for the models. Interestingly, reasoning enhancements can even have a negative impact on simpler problems; (4) Quantization impacts different capabilities unevenly, with significant drop on logical reasoning and minimal impact on text generation. Based on these results and findings, we design an model selection handbook enabling users to select the most cost-effective models without efforts.

cs.AI↗

arXiv2018

Greening Internet of Things for Smart Everythings with A Green-Environment Life: A Survey and Future Prospects

Tremendous technology development in the field of Internet of Things (IoT) has changed the way we work and live. Although the numerous advantages of IoT are enriching our society, it should be reminded that the IoT also consumes energy, embraces toxic pollution and E-waste. These place new stress on the environments and smart world. In order to increase the benefits and reduce the harm of IoT, there is an increasing desire to move toward green IoT. Green IoT is seen as the future of IoT that is environmentally friendly. To achieve that, it is necessary to put a lot of measures to reduce carbon footprint, conserve fewer resources, and promote efficient techniques for energy usage. It is the reason for moving towards green IoT, where the machines, communications, sensors, clouds, and internet are alongside energy efficiency and reducing carbon emission. This paper presents a thorough survey of the current on-going research work and potential technologies of green IoT with an intention to provide some clues for future green IoT research.

eess.SP↗