A Preliminary Exploration with GPT-4o Voice Mode
- URL: http://arxiv.org/abs/2502.09940v1
- Date: Fri, 14 Feb 2025 06:34:08 GMT
- Title: A Preliminary Exploration with GPT-4o Voice Mode
- Authors: Yu-Xiang Lin, Chih-Kai Yang, Wei-Chih Chen, Chen-An Li, Chien-yu Huang, Xuanjun Chen, Hung-yi Lee,
- Abstract summary: This report assesses GPT-4o across various tasks to analyze its audio processing and reasoning abilities.
It shows greater robustness against hallucinations than other large audio-language models (LALMs)
GPT-4o's safety mechanisms cause it to decline tasks like speaker identification, age classification, MOS prediction, and audio deepfake detection.
- Score: 42.17640770852045
- License:
- Abstract: With the rise of multimodal large language models, GPT-4o stands out as a pioneering model, driving us to evaluate its capabilities. This report assesses GPT-4o across various tasks to analyze its audio processing and reasoning abilities. We find that GPT-4o exhibits strong knowledge in audio, speech, and music understanding, performing well in tasks like intent classification, spoken command classification, semantic and grammatical reasoning., multilingual speech recognition, and singing analysis. It also shows greater robustness against hallucinations than other large audio-language models (LALMs). However, it struggles with tasks such as audio duration prediction and instrument classification. Additionally, GPT-4o's safety mechanisms cause it to decline tasks like speaker identification, age classification, MOS prediction, and audio deepfake detection. Notably, the model exhibits a significantly different refusal rate when responding to speaker verification tasks on different datasets. This is likely due to variations in the accompanying instructions or the quality of the input audio, suggesting the sensitivity of its built-in safeguards. Finally, we acknowledge that model performance varies with evaluation protocols. This report only serves as a preliminary exploration of the current state of LALMs.
Related papers
- Audio Large Language Models Can Be Descriptive Speech Quality Evaluators [46.765203628127345]
We introduce the first natural language-based speech evaluation corpus, generated from authentic human ratings.
This corpus offers detailed analysis across multiple dimensions and identifies causes of quality degradation.
We propose an alignment approach with LLM distillation (ALLD) to guide the audio LLM in extracting relevant information from raw speech.
arXiv Detail & Related papers (2025-01-27T22:47:51Z) - Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Language Models [70.99768410765502]
Adrial audio attacks pose a significant threat to the growing use of large language models (LLMs) in voice-based human-machine interactions.
We introduce the Chat-Audio Attacks benchmark including four distinct types of audio attacks.
We evaluate six state-of-the-art LLMs with voice interaction capabilities, including Gemini-1.5-Pro, GPT-4o, and others.
arXiv Detail & Related papers (2024-11-22T10:30:48Z) - Can Large Audio-Language Models Truly Hear? Tackling Hallucinations with Multi-Task Assessment and Stepwise Audio Reasoning [55.2480439325792]
Large audio-language models (LALMs) have shown impressive capabilities in understanding and reasoning about audio and speech information.
These models still face challenges, including hallucinating non-existent sound events, misidentifying the order of sound events, and incorrectly attributing sound sources.
arXiv Detail & Related papers (2024-10-21T15:55:27Z) - Roadmap towards Superhuman Speech Understanding using Large Language Models [60.57947401837938]
Large language models (LLMs) integrate speech and audio data.
Recent advances, such as GPT-4o, highlight the potential for end-to-end speech LLMs.
We propose a five-level roadmap, ranging from basic automatic speech recognition (ASR) to advanced superhuman models.
arXiv Detail & Related papers (2024-10-17T06:44:06Z) - Enhancing Audio-Language Models through Self-Supervised Post-Training with Text-Audio Pairs [3.8300818830608345]
Multi-modal contrastive learning strategies for audio and text have rapidly gained interest.
The ability of these models to understand natural language and temporal relations is still a largely unexplored and open field for research.
We propose to equip the multi-modal ALMs with temporal understanding without loosing their inherent prior capabilities of audio-language tasks with a temporal instillation method TeminAL.
arXiv Detail & Related papers (2024-08-17T18:53:17Z) - Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency [3.161954199291541]
This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o.
GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning capabilities.
The model shows variability and faces limitations in handling complex and ambiguous inputs.
arXiv Detail & Related papers (2024-06-19T19:00:21Z) - An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios [76.11409260727459]
This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system.
We demonstrate that the similarity in phonetics between the pre-training and target languages, as well as the language category, affects the target language's adaptation performance.
arXiv Detail & Related papers (2024-06-13T08:16:52Z) - AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension [95.8442896569132]
We introduce AIR-Bench, the first benchmark to evaluate the ability of Large Audio-Language Models (LALMs) to understand various types of audio signals and interact with humans in the textual format.
Results demonstrate a high level of consistency between GPT-4-based evaluation and human evaluation.
arXiv Detail & Related papers (2024-02-12T15:41:22Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.