Datasets:

Lab-MSP
/

comparative-reasoning-traces

license: other
license_name: msp-podcast
license_link: https://lab-msp.com/MSP/MSP-Podcast.html
language:
  - en
task_categories:
  - audio-classification
tags:
  - speech-emotion-recognition
  - preference
  - reasoning
  - chain-of-thought
  - dpo
pretty_name: Comparative Reasoning Traces for Preference-based SER
size_categories:
  - 10K<n<100K
configs:
  - config_name: arousal
    data_files: data/arousal.jsonl
  - config_name: valence
    data_files: data/valence.jsonl
  - config_name: dominance
    data_files: data/dominance.jsonl

Comparative Reasoning Traces for Preference-based SER

Reasoning traces used to train the SFT-CoT and DPO-CoT models of Comparative Reasoning: Making an Audio Language Model Better at Comparing Emotions (Interspeech 2026).

Models: SFT · SFT-CoT · DPO · DPO-CoT

Each example is an MSP-Podcast v2.0 training pair (10k pairs per attribute) with

a chosen trace that explains why the clip with the higher attribute value is the answer, and
a rejected trace that argues for the wrong clip (used as the rejected response in DPO-CoT).

The traces were generated by Qwen/Qwen3-Next-80B-A3B-Thinking-FP8 from Qwen3-Omni-Captioner audio captions and GeMAPS acoustic features (discretized into low / medium / high) of both clips, and kept only if their final answer was consistent with the label.

The audio is not included. Obtain MSP-Podcast (release 2.0) from the MSP Lab under its license.

Fields

Field	Description
`key`	`<sen1 stem>_<sen2 stem>`
`emotion`	`arousal`, `valence`, or `dominance`
`sen1`, `sen2`	MSP-Podcast file names of Clip 1 and Clip 2
`preference`	`1` if Clip 1 has the higher attribute value, `0` if Clip 2 does
`question`	Question asked to the audio LM, after the two clips (Clip 1 = `sen1`, Clip 2 = `sen2`)
`answer`	`Clip 1` or `Clip 2` (ground truth)
`reasoning`	Chosen reasoning trace
`reasoning_source`	`blind`: the reasoning LLM was not given the answer and reached the correct one; `guided`: regenerated with the ground-truth answer given, for pairs the blind mode got wrong
`rejected_answer`	The wrong answer
`rejected_reasoning`	Reasoning trace generated with the wrong answer given (`null` for 5 pairs where verification failed)

Training format

The audio LM gets a system prompt, the two clips, and question:

system: You are a helpful assistant for emotion comparative reasoning.
user:   <audio: sen1> <audio: sen2> {question}

The training response is <think> {reasoning} </think> <answer> {answer} </answer> (SFT-CoT, DPO-CoT chosen), and <think> {rejected_reasoning} </think> <answer> {rejected_answer} </answer> for the DPO-CoT rejected response.

Usage

from datasets import load_dataset

traces = load_dataset("Lab-MSP/comparative-reasoning-traces", "arousal", split="train")

To build the ms-swift training data, download data/*.jsonl into a folder and run data/post_process/make_swift_data.py --traces_dir <folder> from the code repository.

Citation

@inproceedings{naini2026comparative,
  title     = {Comparative Reasoning: Making an Audio Language Model Better at Comparing Emotions},
  author    = {Naini, Abinay Reddy and Kim, Jaeyeon and Yang, Chao-Han Huck and Watanabe, Shinji and Busso, Carlos},
  booktitle = {Interspeech},
  year      = {2026}
}