EmotionThinker: Prosody-Aware Reinforcement Learning for Explainable Speech Emotion Reasoning
Authors: Dingdong WANG
Shujie LIU
Tianhua Zhang
Youjun Chen
Jinyu Li
Helen M. Meng
Venue: ICLR 2026 Oral (2026)
Keywords: Speech Emotion Recognition
Speech LLMs
Speech Processing
Reinforcement Learning
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Emotional information in speech plays a unique role in multimodal perception. However, current Speech Large Language Models (SpeechLLMs), similar to conventional speech emotion recognition (SER) systems, still treat emotion understanding as a simple classification problem. This provides limited interpretability of predictions, while leaving the LLMs’ expressive and reasoning capabilities underutilized. In this work, we take the first step to reformulate SER as a deep reasoning problem through reinforcement learning (RL). We propose EmotionThinker, which is designed to generate accurate emotion predictions with interpretable explanations grounded in fine-grained acoustic cues. To achieve this, we first construct EmotionCoT-35K, an emotional reasoning dataset with Chain-of-Thought annotations and detailed captions. Second, we observe that current SpeechLLMs exhibit weak prosody perception, whereas prosodic cues constitute fundamental signals for interpreting emotions. To address this, we develop the prosody-enhanced foundation model EmotionThinker-Base, and demonstrate that prosody enhancement improves emotion understanding. Third, we introduce Group-Relative-Policy-Optimization with Progressive-Trust-aware-Reasoning-Reward (GRPO-PTR}) for RL. Different from standard GRPO, which relies only on rule-based outcome rewards, GRPO-PTR progressively introduces reasoning reward, dynamically adjusts it with a trustworthiness weight reflecting the alignment between reasoning and outcome, and evaluates the overall reasoning quality with a reward model based on multi-dimensional criteria. EmotionThinker outperforms previous state-of-the-art evaluation models both in emotion accuracy and explanation quality, advancing SER toward interpretable multimodal reasoning.

EMOTIONTHINKER, PROSODY-AWARE REINFORCE- MENT LEARNING FOR EXPLAINABLE SPEECH EMOTION REASONING
Dingdong 2 Liu', Tianhua Zhang', Youjun Chen', Jinyu Li,, Helen Meng* 'Tee Chinese University of Hong Kong MMicosooft Corporation di dingdongwangel ink. cuhk. edu.hk
ABSTRACT
Emotional information in speech plays a unique role in multimodal perception. However, current Speech Large Language Models (SpeechLLMs), similar to ventional speech emotion recognition (SER) systems, still treat emotion under- standing as simple classification problem. This provides limited interpretability of predictions, while leaving the LLMs' expressive and reasoning capabilities underutilized. In this work, take the first step to reformulate SER as a deep reasoning problem through reinforcement learning (RL). We propose Emotion- Thinker, which is designed to generate accurate emotion predictions with inter- pretable explanations grounded in fine-grained acoustic cues. To achieve this, we first construct EmotionCo
an emotional reasoning dataset with Chain- of-Thought annotations and detailed captions. Second, we observe that current SpeechLLMs exhibit weak prosody perception, whereas prosodic cues consti- tute fundamental signals for interpreting emotions. To address this, we develop the prosody-enhanced foundation model Emotion Base,, and demonstrate that prosody enhancement improves emotion understanding. Third, we introduce Group-Re Saatiive-Poley-Oplii
with Progressive-Trusse Trust-aware- Trust-awarr-Reasonning Reasoning- Reward (GRPO-PTR) for RL. Different from standard GRPO, which relies only on
outcome rewards, GRPO-PTR progressively introduces reasoning re- ward, dynamically adjusts it with trustworthiness weight reflecting the alignment between reasoning and outcome, and evaluates the overall reasoning quality with a reward model based on dimensoaaan criteria. ImnotoonThinker outperforms previous state-of- ofte-offtthe-rt evaluation models both in emotion accuracy and explana- tion quality, advancing SER toward interpretable multimodal reasoning. Project page: http:///sthlll.ooommmooogggg//nn
INTRODUCTION
Advances in speech large language models (SpeechLLMs) have demonstrated impressive perfor- mance across various speech downstream tasks (Arora et al., 2025; Su et al., 2025; Ji al., 2024a). Among them, speech emotion recognition (SER) is particularly important for numan-computce interaction and affective computing systems (Wani al., 2021; El Ayadi al., 2011). Nevertheless, current SpeechLLMs largely inherit the paradigm of conventional SER systems, treating emotion understanding as a categorical classification problem (e.g., assigning a speaker to a discrete emotion label). Such formulations limit interpretability, while leaving the expressive and reasoning capabilities of multimodal LLMs underutilized. This gives rise to one intuitive thought: Can SpeechLLMs reason like humans about "why" they make emotional judgments?
Recent efforts have explored enhancing emotion explainability through supervised fine-tuning (SFT) (Xu al., 2024; Liang al., 2024; Zhang & Poellabauer; Wang et al., 2025e; Thimonier et al., 2025; Chen et al., 2025b; Zhang et al., 2025), prompting models to generate predictions alongside acoustic captions. However, these methods primarily operate at the descriptive level, failing bridge
Work done during internship Microsoft.

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Traditional ERM
Query:
Cannot explain "why"; Simple classification problem.
Answer: Angry
What is the emotion of the speaker in the audio?
(b) Ours: Emotion-Thinker
20
The speaker, an adult nith a loud volume, harsh fone, and fast speech rate, the intonation contour rises sharply Scalln the stressed word "what" - Thus, combined with this clearly expresses anger.
Think:
speaker traits
prosody captions
That is WHAT happened!
4 semantic cues
L logical reasoning
Defailed audio captions: Derive judgment with explanation.
Answer: Angry
Figure 1: ThokeonThinker generates emotion predictions with explanatory reasoning by leveraging speaker traits, prosody, semanties, and logic, contrast to traditional models that provide only categorical outputs.
acoustic observations with explanatory reasoning about their emotional significance. In this work, we take the first step towards Reinforcement Learning (RL)-based explainable emotion reasoning. To this end, we identify three key challenges: (i) Scarcity of high-quality datasets: existing emotion corpora lack fine-grained acoustic annotations necessary for reasoning supervision; (ii) Weak prosody perception in foundation models: current SpeechLLMss struggle with acoustic cues particularly prosody (e.g., stress, intonation, pitch), which plays crucial role in conveying emotions; (iii) Limita- tions of standard rule-based RL rewards: optimizing only rule-based rewards (e.g., outcome accuracy) is insufficient for supervising reasoning quality during RL. As result, the model may converge to sub- sub-optimal reasoning strategies that yield the correct outcome but undermine trustworthiness.
To address these challenges, propose EmotionThinker. RL-enhanced SpeechLLM framework that offers the following advantages: (1) higher emotion recognition accuracy: (2) deep reasoning ability to integrate emotion-related cues for justification; (3) fine-gained audio caption covering speaker traits, prosodic cues and semantic information. Specifically, in the first stage, we curate EmotionCoT-335K, high-quality Chain-of-Though (CoT) dataset synthesized from open-source emotion datasets, which includes detailed acoustic cues for emotion reasoning. In this stage, we develop an automated annotation pipeline that extracts speaker traits (gender and age group). prosodic features (c.g., gender, age, pitch, speech rate, energy, stress, intonation), and transcription content. We use this pipeline construct CoT training data via carefully designed prompts. In the second stage, prior to RL training, we construct Emotion Thinker-Base, a prosody- prosody-enhanced enhanced foundation model based on Qwen2.5-0mmi-778 (Xu et al., 2025b). We find that, as prosodic signals are core carriers of emotional intent, enhancing prosody perception allows the model to effectively ground its reasoning affective cues and further improve emotion accuracy.
Finally, during RL training, to address the challenge of open-ended reasoning process, we propose -ecal--eelalvPeeeeeeeeee Opilic--ptiiizaiionnn with Progressive- Trteessiiie--tut----raee Trust-Aware Reasoning-Rewe Reward (GRPO- PTR) reward scheme that measures the intermediate reasoning quality across multiple dimensions. In this stage, we progressively introduce a thinking reward model trained on annotated reasoning responses of diverse quality. This reward model evaluates the reasoning process based criteria such as factual alignment and interpretative quality. Furthermore, to mitigate the risk of reward hacking, incorporate additional trustworthiness weight that evaluates reasoning reward reliability across a group of samples for a given query. This weight penalizes situations when high reasoning rewards are mistakenly assigned to incorrect answers, helping to suppress spurious reward signals and ensure -ottooomg alignment. Experiments show that GRPO-PTR is effective for emotion reasoning, surpassing rule-based RL baselines both in reasoning quality and emotion accuracy. Building on this framework, Emotion Thinker consistently outperforms 16 open-source SpeechLLMs across multiple benchmarks, delivering higher recognition and reasoning ability.
RELATED WORK
SpeechLLMs for emotion understanding. Recent research on SpeechLLMs for emotion under- standing can be broadly categorized into three directions: (1) General-purpose SpeechLLMs such as SALMONN (Tang et al., 2023), Kimi-Audio (KimiTeam et al., 2025), and Qwen-Audio series (Chu et al., 2023; 2024; Xu et al., 2025b) that treat emotion recognition as one of their downstream
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(a) Data Collection
(c) Reasoning LLM
(d) Generated CoT Data
</>
Sources: lemocop//elld/eepeee mmeee//pssm///eeesss///.
-iit GPT-40 API
Example:
-ilterinng -CTT Prompt:
Shatistics:
<thinls The speakee a midde--ged femali, delivers the line wits high pitch level, which ofhen corweys excilement enthusiasn. The energy level normal but the combination of a high pitch and the axpresatee infonation contour suggists a lively and engoging tone. The inforation likely includes dynamic risse and folls, witch are chanacterenic of emot snstions like joy or excifement, as they add sarce of livelnees to the speeth. telvThe stress on the words "sig" and "three" further emphasizes the plyyfll and asimated nature of the statement, Stressing bilg" highlights the action's magnifuda, while emphastaing "three" adds
200 hours
35k camples
You and andysts Your tock
enational
Exomple:
Transcriphion:
provide detalled ressoning explenation of why the speaker likely expressing the enation iabeled as anotior]]
Emotion: Happy
Related prosody cves as following
(b) Key Feature
Extraction
"trancrpptinn" "(tranecriptior"
"tpeoker.ginder" gender"
Prosody features.
"Heenggender__naa [ew]]]
mytreii, almost thythime quality to the delvery, which con exoke sense of or deight. spesch
Energy level
"xpasker_ag"" aage) "(ihrrd ege, "inell
normal
Pitch level
Javel: "[iter pheh_iiie 3I"
which keeps delivery rushed or andous under tones, VnttSennniiclll, content the monscripoor-desriii payful octions like "gMing big push to owing" "hopping three lighteeartd
deiberate, avoiding any
Speed
Intonation contour style
Important: Assume you do not know the ground truth label and suppoce you
Stress
lisened this audio Like a tumen
-aggns
Speaker traits:
listnne, infer the most Ikely amotion
speaker's reinforces
the high
Gender / level
through step-by-ahep reasaning..
pitch expressive infonation suggest
Semantic tralts:
sharing emation c/fhints canswerrhoppcc/eeee /anewer
Transcriphion
Requirements:
Stressed words
Figure 2: EmotionCo data curation pipeline.
evaluation tasks; (2) Empathetic dialogue systems (Xue al., 2024; Chen et al., 2025a; Lin et al., 2024) (c.g., ELSP-Emo (Wang et al., 2024), OSUM-Echat (Geng et al., 2025)) that utilize emotion recognition prerequisite for generating contextually appropriate responses with high emotional intelligence; (3) Descriptive emotion caption models (Xu et al., 2024; Liang et al., 2024; Zhang & Poellabauer; Wang et al., 2025c;; Thimonier et al., 2025; Chen et al., 2025b; Zhang et al., 2025), which describe emotions in natural language alongside contextual information such as transcription and acoustic features. However, previous work across all categories shares fundamental limitation: they focus primarily on improving emotion classification accuracy through architecture design, while lacking deep reasoning capabilities. Even caption-baser based approaches that attempt to enhance inter- pretability still suffer from limited granularity and lack causal links. They provide only surface-level descriptions without establishing systematic connections between acoustic features and emotional inferences. These gaps highlight the need for SpeechLLMs that move beyond categorical prediction to deliver deeper reasoning.
Multimodal Large Reasoning Models. Recent progress in reinforcement learning (RL) has high- lighted its strong potential for enhancing reasoning capabilities across diverse domains (Shao et al., 2024; Guo et al., 2025; Wang et al., 2025a; Xu et al., 2025a). Notably, DeepSeek-RI R1 (Guo et al., 2025) demonstrated that RL with simple rule-based rewards can effectively incentivize robust reasoning without dense supervision, offering a scalable and efficient paradigm. Building on these advances, vision-language models (VLMs) have achieved remarkable success in multimodal reasoning (Wang et al., 2025d; Huang et al., 2025; Shen et al., 2025), such in visual quality assessment (Wu et al., 2025) and image generation (Tong et al., 2025). However, the effectiveness of RL remains largely underexplored in the speech domain. In particular, speech emotion reasoning presents unique mppelity-specific challenges. Accurate emotion understanding requires integrating diverse acoustic cues, especially prosodic features such as stress, tone, and intonation. Yet titcome-bbsed reward (Guo et al., 2025) alone cannot ensure these signals are correctly and comprehensively captured throughout the reasoning process. To achieve trustworthy emotion reasoning, additional mechanisms are needed to supervise intermediate reasoning steps and ensure their alignment with the final predictions.
3 METHOD
We propose a three-stage framework to equip EmotionTTinker with explainable speech emotion recognition (SER) capabilities via explicit reasoning. First, we construct EmotionCoT-35K, a training dataset with fine-grained prosodic and reasoning annotations. Next, we develop Emotion Thinker Base based on Qwen2.5-0mmi-7B (Xu et al., 2025b) by prosody- cold-start training. Finally, we apply reinforcement learning with our proposed GRPO- -PTR strategy to progressively refine reasoning quality while maintaining emotional consistency.
supervised fine-tuning and the
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3.1 EMOTIONCOT-35K
Overall information. Existing speech emotion datasets are limited in offering reasoning-style supervision and detailed prosody-aware annotations. To facilitate explainable SER, we construct EmotionCoT-35KK, richly annotated training dataset of 35,000 speech- pairs spanning about 200 hours of audio. Each sample is annotated with an emotion label from nine common categories (Neutral, Happy, Sad, Angry, Contemp//issust, Confused, Whisper, Surprise, Fear). The dataset is sourced from IEMOCAP (Busso et al., 2008), MELD (Poria et al., 2019), Expresso (Nguyen al., 2023), MEAD (Wang et al., 2020), and EARS (Richter et al., 2024), covering broad range of speakers, acoustic conditions, and conversational styles. As summarized in Table I, EmotionnoT-335K provides not only wider acoustic granularity but also uniqueness in emotion-centered reasoning. To our knowledge, it is the first prosody-aware CoT dataset tailored for SER, enabling models to produce both emotion label and perceptually grounded explanations.
Table 1: Comparison of different speech captioning datasets across various acoustic features. Reasoning denotes the availability of emotion reasoning with chain-of-thought (CoT) annotations.
Dataset
Reasoning
Acoustic Feature
Age
Gender
Emotion
Pitch
Speed
Energy
Style
Contour
Stress
PromptSpeech (Guo al., 2023)
X
Expresso (Nguyen et ol., 2023)
X
X
EARS (Richter al., 2024)
x
X
SpeechCraft (Jin et al., 2024)
x
/
x
/
Speech et 2024b)
CapSpeech (Wang al, 2025b)
X
EmmationCoT:358
Dataset construction pipeline. As shown in Figure 2, we develop an automated annotation pipeline for constructing EmotionCooT335K. In contrast to traditional manual annotation, this approach is cost- efficient and less prone to human bias, particularly for perceptual variables such as pitch, speed, and energy. Specifically, for feature selection, prioritize acoustic cues most relevant human emotion reasoning, ensuring their utility for affective inference. We extract low-level features (speed, pitch, energy) using standard speech processing tools, and identify stressed words from transcriptions using WhiStress (Yosha et al., 2025b). From frame-level pitch-energy energy trajectories, we derive intonation contours and apply Lavitzky-Golay smoothing. The smoothed contours are then classified into coarse styles (expressive vs. flat) and fine-grained intonation patterns (rising, falling, sssing-falling.. Balling-rising! addition, a wav2vec2.0-bor attributes, including gender and age group. Building on these prosodic annotations, as contextual prompts for GPT-4o to generate step-wise reasoning traces, with outputs formatted in <think> and <answer> tags. Details of pipeline and dataset examples provided in the Appendix.
classifier (Baevski et al., 2020) provides speaker
compile them
3.2 PROSODY-CENTRES SUPERVISED TINE-TUNING (SFT)
Directly applying the Qwen2.5-0mni--7 backbone for RL is ineffective, as experiments (Table 5) show that it lacks fine-grained prosody perception- an essential prerequisite for reliable emotion reasoning. For example, humans often rely on prosodic cues such as pitch, energy, and rhythm to infer emotions from speech. An angry utterance is typically characterized by high energy and sharp intonation, whereas a sad one tends to exhibit low pitch and slow speaking rate. These prosodic patterns serve as important perceptual signals for emotional judgment, especially when lexical content alone is ambiguous or emotionally neutral.
Thus we introduce a prosody-centric supervised fine-tuning (SFT) stage to build EmotionnTinkerr Base prior to GRPO-based RL (Shao al., 2024). The SFT corpus (~5000 hours) integrates (i) word-level stress perception using Stress- 17K dataset ( (Yosha et al., 2025a), (ii) prosodic attribute classification tasks derived from expressive ASR data, where the model learns to categorize different pitch, energy, speed, and intonation levels, (iii) comparative prosodic augmentation tasks, where the same utterance is systematically modified with varied pitch, energy, and speed levels, then concatenated in different sequences (e.g., high pitch ~> low pitch - medium pitch), and the model trained to identify the correct ordering patterns, and (iv) 5K EmotionCoT samples to expose the model to basic reasoning patterns for the cold-start training. We jointly optimize the audio encoder,
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Reference Model
Q₁
Amm
Query
Policy Model
Reward Model
R
Normall
Group
Aa
-Zal Iom
e.c
O
Reward Functions
Ry
Ax
Format Reward (Ry]
Ressoning Reward (Ri)
cthinko </thinks
R2
canswers </answer>
R2
Factual Alignment
x
<thinks (--) </thinks
Outcome Reward (R,):
canswer> happy clanswer>
Accuracy: Correct/incornect
Interpretative Qual.
04
Ry
unstable
stable
Figure 3:
Architecture of EmotionThinker with the proposed GRPO-PTI framework. The upper part depicts the high-level GRPO-PTR training pipeline. where only the policy model is optimized. The lower part details PTR strategy, which progressively introduces reasoning reward to stabilize training and enhance reasoning.
audio adapter, and L.LM backbone during SFT. This training stage equips Thintkonn with strong prosody perception ability and an initial grasp of reasoning structures, providing solid foundation for subsequent RL training. Emotion Beationnhhinkr-BBee Base details are provided in the Appendix.
3.3 REINFORCEMENT LEARNING FOR EMOTION REASONING
Figure illustrates the RI. architecture of EmotionnThinkes We follow the GRPO paradigm (Shao et al., 2024) with rule-based rewards. To further supervise reasoning quality, we train reward model and propose the GRPO-PTR, which progressively guides the reasoning process.
3.3. RULE-BASED BASED REWARDS
Format reward. The format reward, denoted Rú, enforces adherence to predefined XML-style schema. Specifically, the reasoning content must be enclosed within <think>, ... </think> and answer>> Formally, for output o,
the final prediction within <answer>.
1, 0, otherwise.
follows the format schema,
Rí(o) =
Outcome accuracy reward. The outcome reward (R,) verifies whether the predicted emotion label matches the ground truth. For given instance with gold label y" and model prediction ğ extracted from <answer>, the reward is defined as:
I j=y",
R. v
0, otherwise.
Together, these rule-based rewards ensure that the model produces structurally valid outputs while maintaining correctness at the outcome level.
3.3.2 GRPO-PTR: PROGRESSIVE TRUST-AWARE REASONING REWARD
Standard GRPO only supervises the final outcomes without constraining the intermediate reasoning process. While in emotion reasoning, this paradigm is prone to shorteut reward hacking; models may generate superficially plausible but logically unsound or perceptually ungrounded reasoning as long the final answer is correct. To address this limitation, we propose GRPO-PTR (Progressive Trust-aware Reasoning Reward), which augments GRPO with open-ended reasoning supervision to guide the model toward accurate and coherent outputs.
To achieve this, we first train a reasoning reward model that evaluates the quality of open-ended reasoning across multiple dimensions. The model is built upon Qwen2.5-0mni-4 (Xu al.,

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2025b) and fine-tuned on 101,400 rg. tuples, where q is emotional prompt, r' model- generated reasoning trace, and E {1...., ......,,j- 5)4 a vector of criterion-specifie scores. The training data constructed by extending EMOTIONCOT-357 with OPT--4--ynnthsizee reasoning variants at controlled quality levels along each criterion. Details are provided in the Appendix. During inference, the reward model outputs JSON object with four fields: factual_alignnen _alignment, interpretative_ caption_completeee completeness, and fluency_an aatrccyaaadddee strusstuuuuaaaat _lariit__ each rated on a 1-5 scale. These ratings then normalized and aggregated into scalar reasoning reward R2 as follows:
Rs
W fj
where Øj
9i
Σ wi
1, Wj 20.
Moreover, since R2 only proxy of the unobserved reasoning quality and not directly tied to task correctness Y {0, 1], it can be unreliable when incorrect responses receive higher R₂ scores while correct ones may appear low within sampled response group. Directly combining such noisy rewards with R, risks biasing the policy toward spurious reasoning patterns. We addresses this issue by assigning a trustworthiness weight r that modulates the contribution Rg based on its alignment with outcome signals. Specifically, let R2 E {0,1) denote the correctness label of the i-th response, where R = 1 indicates the correct prediction and Ry = 0 indicates the incorrect onc. We partition the responses into two disjoint sets:
Gooeeat {i R 1), 1},
Gwrnng = li R = -0.
We compute the mean reasoning reward in each group,
R(OO
R
R(w)
R
Georect
G iEQwse
and define
1, exp( R(0)
R 2 (((m) R(O) R(
7
R(
)
This weight shrinks the influence of RG when its distribution does not separate correct from incorrect responses (i.e., when R < R(('),, reducing the risk of reinforcing misleading reasoning trajectories. In essence, serves group-level alignment gate: only when the sampled response exhibits global agreement between reasoning quality and outcome correctness does R; contribute to the reward signal.
The overall reward for GRPO-PTI optimization as follows:
R₁ = Ry + a.R, + a,t Rs
where denotes the weight of each reward component. To ensure stable optimization, adopt a progressive reward scheduling strategy: during the early training stage, we optimize solely with the outcome reward R. and format reward Ry until emotion accuracy consistently achieves certain level (e.g., 50% accuracy). Introducing multiple unstable rewards too early can cause random fluctuations any component to dominate the reward sum and produce incorrect advantage signals, thereby impeding model convergence. Since the reasoning reward RG is particularly challenging to optimize due to the diversity open-ended generation, we delay its incorporation until the model has stably adapted to the rule- based rewards. This progressive strategy effectively mitigates early-stage instability and ensures more stable reward optimization.
EXPERIMENT
4.1 EXPERIMENTAL SETUP
The RL post-training is conducted on 30K samples from EmotionCoT-35K training data. For evaluation, we adopt four widely used SER benchmarks: IEMOCAP (Busso et al., 2008), MELD (Poria et al., 2019), RAVDESS (Livingstone & Russo, 2018), and SAVEE (Jackson & Haq, 2014). Among them, we use the IEMOCAP and MELD test sets, while RAVDESS and SAVEE evaluated in zero-shot setting.
Datasets and evaluation.

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Table 2: Performance comparison across models. Emotion recognition is measured by accuracy (%), while rea- soning quality is assessed on the overall test dataset on four dimensions: Factual Alignment (FA.), Interpretative Quality (IQ.), Caption Completeness (CC.). and Fluency and Structure (FS.), each on a 5 point scale. Top two results are highlighted in bold and underline, respectively.
Models
Emotion Recognition Accuracy 1
Emotion Reasoning
IEMOCAP MELD RADESS SAVEE Avg
FA.
IQ.
CC.
FS.
Avg
General Speech Large Language Models
GLM-4-Voice
22.38
21.43
19.67
20.84
21.29
1.95
2.62
2.17
3.22
2.49
BLSP
41.24
50.47
11.10
10.77
36.02
1.01
2.44
1.63
2.71
1.94
DIVA
40.15
35.19
18.28
20.14
31.87
2.33
2.96
2.68
3.65
2.90
MERaLiON
45.66
37.98
12.43
16.25
33.09
1.17
2.03
1.88
3.54
2.16
MERaLiON2
51.05
51.10
37.02
25.43
46.09
2.41
3.20
2.72
3.83
3.04
SALMONN
23.82
31.32
20.38
17.86
25.61
1.32
2.11
1.03
2.89
1.84
Qwen-Audio- Chat
38.80
55.70
70.11
71.53
54.86
1.98
2.40
2.07
2.79
2.31
Owen2-Audio-Instraa
37.71
51.23
64.98
65.13
51.14
1.70
2.27
1.90
2.85
2.18
Kimi-Audio
57.72
59.13
61.07
55.21
58.83
2.45
2.75
2.35
3.34
2.72
Emotion Focused Speech Large Language Models
SECap
36.29
34.20
28.34
21.93
32.64
1.82
1.13
1.00
2.31
1.56
OSUM-EChat
41.49
53.38
37.34
24.27
44.04
2.18
2.59
2.21
3.19
2.54
BLSP-Emo
76.00
57.30
72.00
63.73
65.41
2.33
2.78
2.45
3.37
2.73
Omni Large Language Models
Phi-4-Multimodal
36.62
39.81
17.63
15.55
32.15
2.12
2.67
2.51
3,43
2.69
Segrrz-3B-Omm
14.77
21.89
21.45
16.97
19.25
2.10
2.56
2.41
3.33
2.61
MiniCPM-O
35.54
52.78
40.93
35.47
43.60
2.53
3.00
2.71
3.81
3.01
Qwen2.5-Omni-7B
45.70
54.64
64.77
52.49
50.83
2.32
2.91
2.64
3.59
2.87
EmotionThinkes
77.68
59.71
71.56
73.96
68.89
3.54
4.01
3.96
4.42
3.98
Baselines. To comprehensively benchmark the emotion reasoning ability of EmotionThinkar we compare against 13 general-purpose SpeechLLMs and OmniLLMs (GLM-4-Voise (Zeng al., 2024), BLSP (Wang et al., 2023). DIVA (Held et al., 2024), MERaLiON (He et al., 2025), MER- aLiON2 (He et al., 2025), Qwen-Audio- Chat (Chu et al., 2023), Qwen2- Audio Audio-Instruct (Chu et al., 2024), Kimi-Audio (Kimi Team et al., 2025), Phi-4-Multimodal (Abouelenin et al., 2025), Megrez- 3B-Omni (Infinigence AI, 2024), MiniCPM-O (MiniCPM-o Team, 2024), Qwen2.5-Omni-D (Xu et al., 2025b)) and 3 al., 2024), OSUM-EChat (Geng al., 2025)).
SpeechLLMs (BLSP-Ema (Wang et al., 2024), SECap (Xu
which is trained Implementation details. Emotion Thinker is initialized from post-training runs for 3,000 steps for one epoch on 500+ hours of prosody-enhancee speech data. RL R
with KL divergence coefficient of 0.04, learning rate of 1 x 10-6, and K 8 sampled candidates per input. The overall reward weighted sum of accuracy, format, and reasoning signals with weights 1.0, 0.3, and 0.5, respectively.
4.2 MAIN RESULTS
We assess model performance from two perspectives: unweighted accuracy for emotion recognition and GPT-based ratings on 5-point scale for reasoning quality. The reasoning evaluation covers four dimen- sions: (1) Factual Alignment (FA): whether the ex- planation accurately reflects speaker traits, semantic content, and prosodic cues in the audio; (2) Inter- pretative Quality (IQ): how clearly and convincingly these connected to the emotion outcome; (3)
Table 3: Human evaluation results on emotion
reasoning based on consistent 100-sample set.
Models BLSP-Emo
FA.
1Q.
CC.
FS
Avg
1.2
2.0
1.5
2.5
1.8
OSUM-EChat
1.5
2.5
1.3
2.5
2,0
Kimi-Audia
2.6
3,4
1.6
3.5
2.8
Qwen2.5-Omni
2.4
4.2
3.0
4.5
3.5
TmotionTThinkrr
3.7
4.5
4.7
4.5
4.4
Caption Completeness (CC): the degree to which the caption covers all relevant acoustic and semantic information in comprehensive manner; and

---

Siven2.5-Omn--78
Question: What the emotion expressed in this audio clip? (sad)
cthinks The amolion expressed is the audio dip is 'angryy. This indicaled by the fone and intensety of the speaker's voice, which suggests Frustration or irritation.
EmotionThinker-Be Bose + GRPO:
ethinks speokerk tone suggetts o stote of and. energy level relatively steady, and white particularly low, could Imply a lock strong emotion...vnle InVshhe transcriptionn"ae "Okay I'm sorry, but just stood in this line for on hour" hour"rreveds a contect in which the speaker is apologizing for arriving iste, posstbly due to being delayed by something exciting or importont. The use of the word "sorny" seems more sancastic than sincere, especially when poined with an upward intonation pal paltern that hints el playful Gefacelion.Overall the speech likely reflects playful annoyance or energelic sarcesm, rather than any negafive emolion such as frustration or sedness wtt.:///hwhoreeeeessssseeeeeee
ThotionThinker (GRPO-PTR):
cthinks The speakes, teanage femole, conweys serse of amotional tension through aeversl prosodic and aspustic cuss. The pitch level notably low, which often signals subdued or restroined emotional state, potentallly indicative of sodness This further supported by the low energy level, which Suggesis the speaker not eserting ling much vocal effort common trail when someone feels drained exciperaled, VelaThe speech rale is normal, which rules heightened the duration of their experience, which likely feets burdersome unfair then. This stress peftern digns with the semantic content the transcrition, where the spesker mentions
but the stress placed
Vhour' stands out By emphosizing the word, the spesker drows attention
inpolience, reinforcing emolional lone.. extended pertod: 1'1 lagether, these prosedic h/hiinkcoorswerrree /oo////////////eeeeerr
lnc for houn The phrosing itself enthusiaio palisnce. Taken
dissatisfaction
pilch and energy- sugouils combined with the semantic context strongly paint to
delivery
of
Figure 4: Case study comparing emotional reasoning across models sad utterance. Red highlights mark descriptions inconsistent with the ground truth, whereas green highlights indicate correct evidence.
Fluency and Structural Clarity (FS): measures the grammatical correctness, readability, and logical organization of the reasoning. Detailed prompt and rubric are provided in the Appendix.
As shown in Table 2, EmotionThinkes achieves state-of-the-art performance on both overall emotion recognition accuracy and reasoning quality. For emotion recognition, it demonstrates superior accuracy on most benchmarks, achieving an overall average of 68, 89%. This surpasses the second-best model, BLSP-Emo, by approximately 3%. Notably, most general-purpose models (e.g.. Kimi-Audio) outperform the other two imotion-focusee SpeechLLMs (SECap and OSUM-EChat), underscoring the need for further advances in emotion-speecialized SpeechI.LMs. For reasoning quality, most baselines lack native structured reasoning, except Qwen2.5-Omn with <think><casswe>> formatting. To clicit their latent reasoning ability. we apply a unified prompt that requests justifications grounded in speaker traits, prosodic cues, and semantic content. EmotionThinker achieves the highest average score (3.98), substantially outperforming all models across reasoning dimensions. Example reasoning outputs per model are included in the Appendix.
To mitigate potential biases in GPT-based evaluations, we conduct additional human assessment on five representative models using 100 anonymized outputs (20 per model), which are rated by four independent reviewers. As shown in Table 3, Emotion'Thine across all dimensions, with particularly stronger performance in Factual Alignment (3.7) and Caption Completeness (4.7). The model performance rankings align closely with GPT-based evaluations, indicating robust agreement between human and automatic judgments.
demonstrates clear superiority
4.3 ANALYSIS
To validate the effectiveness of our Emo- conduct
tionThinker framework, comprehensive analysis. We first exam- ine the prosody perception capabilities of EmotionThinker-Basse Second, we com- pare supervised fine-tuning and reinforce- ment learning under the same training data to assess the benefits of reward-driven optimiza- tion. Third, we perform component-ww component-wise ab- lations to examine the contribution of core components in our proposed GRPO-PTR framework, accompanied by a case study. Fi- nally, we analyze the impact of key hyperpa- rameters, including sampling size and reward weighting.
Table 4: Ablation study on different training strategies for average speech emotion recognition accuracy (SER) and emotion reasoning (ER) score, evaluated on the overall test
dataset. Variants V1 V6 are built upon Baseline 2.
SER
ER
Variations
Training Strategy
(Acc)
(Score)
Baseline 1 Baseline 2
Qwen2.5. 5-Omni-7B
50.83
2.87
EmotionThinker
52.63
3,41
VI
SET GRPO
53.91
3.78
V2
62.91
3.45
V3
SEPOO-PTR-w/o-rainne
66.67
3.36
V4
67.71
3.74
VS
GRPO-PTR- -e---TTT--r-rressii
62.80
3.76
V6
GRPO-PTR
68.89
3.98
Prosody perception comparison. We con-
struct prosody perception test set by sampling 1,000 unseen utterances from the prosody-centric
8

---

Table 5: Prosody perception comparison across pitch (Pit.), speed (Spee.), energy (Ene.), intonation (Into.), and stress (Stre.). Evaluation is based on accuracy (%)
Table 6: GRPO-PTR with varying K settings.
Settings
Iemocap
Meld
Radess
Savee
Avg
75.61
57.64
70.29
72.85
67.06
Model
Pit.
Spee.
Ene.
Into.
Stre.
K=6
75.31
57.55
71.01
72.55
67.07
K=8
75.82
58.03
70.58
72.91
67.35
Owen2.5-Omni-1
25.71
29.94
27.67
25.83
30.24
K
75.77
57.89
70.59
72.87
67.28
Emotion Thinker-Baar
75.11
68.70
69,42
60.25
71.50
K= 16
75.81
59.22
69.87
72.85
67.66
dataset. As shown in Table 5, PenottonnTinker-Beee demonstrates significantly improved sensitivity to pitch, energy, speed, and stress compared the original backbone. These results validate the necessity and effectiveness of prosody-centric SFT for enhancing prosodic understanding before RL.
Supervised fine-tuning (SFT) vs. reinforcement learning (RL). We compare the SFT with different RL approaches using the same training data from T-35.. As shown in Table 4, SFT (V1) provides moderate gains over the base model, achieving 53.91% % accuracy and 3.78 in reasoning score. Standard GRPO (V2), which relies only on base- rewards, yields a clear improvement. Our proposed GRPO-PTR (V6) further enhances both emotion accuracy and reasoning quality.
As shown in Table 4, we ablate key components of GRPO-PTR: (i) GRPO-PTR SEPO--PTR-wlo-triinee RM, where the reasoning reward is derived from an untrained Owen2.55Ommi w/o-trust, where the trustworthiness weight 7 is removed; and (iii) GRPO-PTEE PEEPOOPTRR-/O--- hwtooooprrresiveee...oooooe where progressive reward combination is disabled and all reward signals are aggregated simultane- ously. Effectiveness of the reward model: When the reasoning reward is derived from an untrained model (V3), both SER accuracy and ER score drop notably (66.67% and 3.36, respectively). This indicates that our trained reward model provides more informative supervision signals, whereas the untrained model introduces noise that weakens both task correctness and reasoning quality. Effect of the trustworthiness weight: Removing the trustworthiness weight (V4) leads to only marginal decline in SER accuracy
model; (ii) GRPO-PTR-
but causes a notable drop in ER score (3.74). This suggests that T helps filter out unreliable yet seemingly plausible reasoning. Effectiveness of progressive reward combination: Disabling progressive scheduling (V5) results in a notable performance drop, with SER accuracy decreasing to 62. 80% and ER score to 3.76. This shows the necessity of integrating a progressive reward integration strategy to improve training stability.
Case study, As illustrated in Figure 4, compared with Qwen2.5 Owwe2.55Ommii77B and EmottonnThnnker Basse + GRPO, GRPO-PTR produces more accurate and comprehensive demonstrates stronger alignment with acoustic and semantic cues, yielding more coherent explana- tions. In contrast, Qwen2.5-0mni provides overly brief reasoning without sufficient justification, while the GRPO variant includes hallucinated interpretations inconsistent with the input.
-rroundd reasoning. It
Ablations on hyperparameter settings. As shown in Table 6, we vary the number of gener- ated responses K, while keeping all other settings fixed during training. Table 7 shows that reducing K from 16 to has only marginal effect, we choose 8 for trade-off between computational cost and accuracy. addition, we conduct sensitiv- ity analysis on two parameters a, and ar, which correspond the outcome accuracy reward and reasoning reward weights. We fix the format reward weight a as 0.3 for all settings. Results suggest that moderate emphasis on thinking reward (a; = 0.5) yields the best overall performance. higher a (a, 1.0) leads to slight performance degradation, likely due to optimization instability introduced by excessive emphasis on intermediate reasoning signals. These results emphasize the importance of maintaining a balanced reward composition in multi-signal reinforcement learning.
Table 7: Sensitivity analysis on reward penalty.
Settings
lemocap Meld Radess Savee Avg
a,=1.0, a,=0.3
77.71
59.68
71.55 73.92 68.87
a,=1.0, a,=0.5
77.68
59.71
71.56
73.96 68.89
a.=1.0, 0,=1.0
73.60
57.46
68.41 66.92 65.52
CONCLUSION
In this work, we present Emotion Thinker, a pioneering RL-based framework that extends speech emotion recognition (SER) from simple classification explainable emotion reasoning. Within the RL stage, we proposed the GRPO-PTR strategy to refine reasoning in a coherent and trustworthy manner. This design enables the model to move beyond surface-level label prediction, yielding

---

explanations that fine-grained logically coherent, and grounded in relevant acoustic and semantic cues. Comprehensive experiments on various benchmarks show that Emotion Thinker achieves state-of-the-art performance in both emotion accuracy and the reasoning quality.
ACKNOWLEDGEMENT
This work supported by the Centre for Perceptual and Interactive Intelligence (CPII) Ltd., CUHK-led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative Region Govermment.
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Prosody-Aware Reinforcement Learning for Explainable Speech Emotion Reasoning
Supplementary Material
CONTENTS
1
Introduction
2 Related Work
3 Method
3.1
EmotionCoT-335K
3.2
Prosody-Centric Supervised Fine- Fine-Tuning (SFT)
3.3
Reinforcement Learning for Emotion Reasoning
3.3.1
Rule-based Rewards
3.3.2
GRPO-PTR: Progressive Trust-aware Reasoning Reward
4
Experiment
4.1
Experimental Setup
4.2 Main Results
4.3
Analysis
8
5
Conclusion
Acknowledgement
10
The Use of Large Language Models
16
EmotionCoT-33K
16
B.1 Data Source
16
B.2 Automatic Annotation Pipeline
16
B.3 CoT Prompt
17
EmotionTThiner-BBn
17
C.I Details of the Prosody-Centric SFT Corpus
17
C.2 Training Details of EmotionThinker
17
D Reward Model
19
D.1 Data Construction Details.
19
D.2 GPT Prompt
19
15

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E Emotion Reasoning Criteria
19
Case Study
19
A
THE USE OF LARGE LANGUAGE MODELS
During this work, we used large language models (LLMs)) as assistive tools for data construction, model evaluation, and paper polishing. For data construction, used the GPT-4o API to synthesize samples for EmotionCoT-355 and to create training pairs for the reward model (see Section B and Section D); For evaluation, GPT-4o was used assess the quality of emotion reasoning under fixed rubric (see Section E). For writing, LLMs were used only for copyediting and phrasing to improve clarity and consistency.
B EMOTIONCOT-35K
B. DATA SOURCE
IEMOCAP (Interactive Emotional Dyadic Motion Capture) (Busso et al., 2008): The IEMOCAP database is an acted, multimodal and multispeaker database, recently collected at SAIL lab at USC. contains approximately 12 hours of audiovisual data, including video, speech, motion capture of face, text transcriptions. It consists of dyadic sessions where actors perform improvisations or scripted scenarios. specifically selected to elicit emotional expressions. IEMOCAP database is annotated by multiple anmotators into categorical labels, such as anger, happiness, sadness, neutrality, as well as dimensional labels such valence, activation and dominance.
MELD (Multimodal EmotionLinee Dataset) (Poria et al., 2019): The MELD dataset has been created by enhancing and extending Emotion dataset. MELD contains the dialogue instances available in Emotionl Lines, but also encompasses audio and visual modality along with text. MELD has more than 1400 dialogues and 13000 utterances from Friends TV series. Multiple speakers participated in the dialogues. Each utterance in a dialogue has been labeled by any of these seven emotions Anger, Disgust, Sadness, Joy, Neutral, Surprise and Fear. MELD also has sentiment (positive, negative and neutral) annotation for each utterance.
Expresso (Nguyen et al., 2023): Expresso, a high-quality (48kHz) expressive speech dataset that includes both expressively rendered read speech (8 styles, in mono wav format) and improvised dialogues (26 styles, in stereo wav format). The dataset includes speakers (2 males, females), and totals 40 hours (11h read, 30h improvised).
MEAD (Multi-view Emotional Audio-visual Dataset) (Wang al., 2020): MEAD is large- scale audio-visual dataset built for research in emotional talking-face generation and cross-modal expression modeling. It includes data from 60 actors and actresses, recorded under eight distinct emotions at three intensity levels (except neutral) in strictly controlled environment.
EARS (Expressive Anechoic Recordings of Speech) (Richter et al., 2024): It consists -00 hours of speech from 107 speakers of diverse demographics (age, ethnicity, background), recorded at 48 kHz in anechoic conditions. EARS covers a broad range of expressive styles: multiple reading styles (e.g. regular, loud, whisper, fast, slow, high pitch, low pitch), non-verbal vocalizations, conversational freeform speech, and speech in 22 emotional styles. Transcriptions are provided for the reading portion, along with metadata (gender, age, race, native language). Its high quality, style diversity, and speaker variation make it particularly suitable for modeling expressive prosody, style transfer, and robust speech enhancement.
B.2 AUTOMATIC ANNOTATION PIPELINE
To enable large-scale, fine-grained expressive speech annotation, this work introduces modu- lar automatic annotation pipeline that systematically extracts multiple prosodic and speaker-level attributes-inclue pitch, energy, speaking rate, word-level stress, gender, and age-usingg bination signal processing techniques and specialized classification models. The core prosodic features, namely pitch, energy, and speed, are derived via classical digital signal processing methods:
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pitch and energy computed from short-time acoustic frames, while speaking rate is estimated by combining phoneme-level alignments from forced alignment tools with utterance duration statistics. For stress annotation, framework that extends a frozen Whisper backhone with a stress detection head comprising a trans- former decoder block and feedforward classifier. It predicts token-level stress labels based on cross-attention over intermediate Whisper embeddings, achieving high accuracy without requiring forced alignment or manual labels. For intonation contour annotation, we first extract frame-level pitch and energy trajectories, then apply a Savitzky- local noise. The smoothed pitch contours are analyzed derive both coarse-graines intonational styles (e.g., expressive vs. flat) and fine-graines melodic patterns (e.g., rising, falling, rising fasinn-falling. falling-rising), which reflect the global prosodic dynamics of the utterance. To capture speaker characteristics, gender and age are inferred using supervised classification models trained atop wav2vec 2.0 (Baevski et al., 2020) representations. Specifically, gender prediction achieves high accuracy by distinguishing between male and female speakers, while age estimation bins speakers into coarse-grainee garsee-rained age categories. These automatically extracted attributes serve as the foundation for natural language description generation, and their accuracy is independently evaluated on external benchmarks, demonstrating reliability across both English and Mandarin domains.
employ the WhiStress model (Yosha et al., 2025b) alignment-free
filter to smooth the curves and reduce
B.3 COT PROMPT
As shown in Figure 5, we use GPT-4o prompt to construct emotion reasoning process. The prompt guides the model to generate structured explanations grounded in both prosodic and semantic cues, enabling CoT-style supervision.
C EMOTIONTHINKER BASE
C.I DETAILS OF THE PROSODY- CENTRIC SET CORPUS
(i) Word-level stress dataset (Stress-17K (Yosha et al., 2025a)). Stress-17K is generated through a synthetic pipeline that varies sentence stress placement to alter utterance meaning, providing paired audio samples with explicit stress annotations. It covers multiple stress categories (contrastive, emphatic, new-information. focus) and offers both detection and reasoning supervision. We directly incorporate this dataset enhance the model's capacity for explicit word-level stress perception.
(ii) Prosodic Attribute Classification Data. We sample utterances from the GigaSpeech (Chen al., 2021) corpus and process them using the annotation pipeline originally developed for con- structing EmotionCoT. This pipeline extracts pitch, energy, speaking rate (speed), and intonation information. Based on these annotations, we construct supervised fine-tuning (SFT) tasks where the model is queried about prosodic categories. For example, given an audio clip, the corresponding question explicitly asks "What the pitch level of this speech?". In this way, the model learns to associate acoustic signals with categorical prosodic attributes.
(iii) Comparative prosodic augmentations. To strengthen robustness, we construct prosodic contrastive data through signal-level augmentations applied to GigaSpeech (Chen et al., 2021) utterances. Specifically, we generate modified versions of each sample with systematically increased decreased pitch, energy, and speaking rate. These variations create sequences exhibiting controlled prosodic shifts. For instance, a concatenated sequence may follow a progression such as low - high + medium energy. Corresponding training questions explicitly ask the model to identify the prosodic ordering pattern. This design encourages the model to perform comparative reasoning over prosodic attributes rather than relying on absolute values alone.
then randomly concatenated to
C.2 TRAINING DETAILS OF EMOTIONTHINKER-BASS
The backbone of ThinkiorhhiinerBBBe Base Qwen2.5-0mmi: (Xu et al., 2025b). The supervised fine-tuning (SFT) process is divided into two stages. For Stage I: train on the constructed Prosody-Cente Centric corpus, jointly updating the audio encoder, audio adapter, and LLM backbone with full-parameter training. To preserve the backbone's instruction-follovinn ability and basic
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Prompt Template (Generate emotion reasoning process)
You are an expert in emotional speech and vocal analysis. You have listened to an audio sample with the following cues:
groundtruth _etttttnhemttonom "[item["gt"]"
transcription' ""trranscriptoon"
speaker_gender "iitem"gender_neee _ew""]]"""""""
speaker_age age "'ag _eevel'"]]]"""
pitch_lever "(item[pitch_ pitchhleeell""'"" level"]]
energy_level' "(itemm"enerrgy [lvee!""""
speed_level" level" "(item["speed leve!]]"
intonation_conton
stressed woors"
Your task is to provide detailed reasoning explanation of **why** the speaker is likely expressing the emotion labeled as "(t"
*Important:** Assume you do **not** know the ground-truth label. Like a human listener, infer the most likely emotion through step-by-step reasoning. Let the conclusion arise naturally from your reasoning process. reasoning process focus should be on *acoustic and prosodic cues**, while semantic content may serve as a secondary reference.
"Requirements:**
Explicitly reference and quoting the "*transcription* in your reasoning Naturally incorporate the speaker profile into your writing (e.g.. middle-aged female speaker").
Discuss key acoustic/prosodic features you hear, such as pitch, energy, speech rate, intonation, stress, etc. Focus features you believe strongly support predicted emotion, and you may omit or downplay others that seem unimportant or irrelevant. Aim for stylistic variation across outputs by using diverse sentence structures and distributing your reasoning across multiple, well-structured sentences.
When analyzing semantic content, consider whether aligns with the labeled emotion. If it does, point out specific cues (e.g., frustration, concern); if not, explain how the emotion may still be conveyed prosodically despite the content.
original given intonation intonation_contour contour "uncertain" ' "too_short", you can ignore this
part analysis.
You may briefly mention stress patterns if they provide insight, you are encouraged to details that seem incidental
emotionally meaningful.
Length: 50 - 200 words. Do **not** exceed 200 words.
Do **not** begin your explanation with the emotion label. Instead, simulate a human reasoning process where the emotional interpretation emerges gradually through observation and analysis.
Only return the emotional reasoning content.
Figure 5: Prompt template used elicit emotion reasoning traces from GPT-4o.
ASR capability, we additionally incorporate 20% text-only data and 20% ASR data sampled from LibriSpeech (Panayotov et al., 2015) and GigaSpeech (Chen et al., 2021). Training is conducted for one epoch with learning rate x 10 For Stage we further sample 5K examples from EmotionCoT for cold-start reasoning supervision. In this stage, only the LLM layers trained with LoRA (Hu et al., 2022) adaptation, while the audio encoder and adapter remain fixed. Training is performed for epochs with learning rate of 10-5
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D REWARD MODEL
D.1 DATA CONSTRUCTION DETAILS
For constructing the training data of the thinking reward model, we build upon the EmotionCoT corpus. We first sample 20K high-quality instances from EmotionCoT, treating them as gold-standary reasoning traces with perfect scores (5) across the four evaluation dimensions: "Factual Aligmment", "Interpretative Quality". "Caption Completeness", and "Fluency and Structural Clarity". To introduce controlled quality variation, we create degraded counterparts by randomly assigning a score from to for each dimension. This procedure produces 101,400 (g.7.9) 7. g) tuples in total, where q denotes the emotional prompt, r the reasoning variant, and denotes the criterion wise score vector, where each clement is integer from to 5. Given the assigned score configuration, GPT-4o is prompted to generate reasoning traces that reflect the intended quality levels. For example, lowering the score on "Factual Alignment" leads to factually inconsistent content, while lower "Fluency and Structural Clarity" score yields disfluent or poorly structured text. This systematic process ensures balanced coverage across quality levels and provides diverse supervision signals for training the reward model.
D.2 GPT PROMPT
To construct reasoning responses, we use GPT-4o with the prompt template shown in Figure 6 and Figure Given a target score configuration across the four dimensions, GPT-4o generates reasoning text that matches the specified quality levels, ensuring controlled variation for the reward model supervision.
E EMOTION REASONING CRITERIA
We obtain 1-5 ratings on four key dimensions of emotion reasoning quality using GPT-4o: (1) Factual Alignment, (2) Interpretative Quality, (3) Caption Completeness, and (4) Fluency and Structural Clarity. The scoring prompt used to elicit these criterion-wise ratings is shown in Figure 8 and Figure 9.
F CASE STUDY
To better understand how different models perform emotion reasoning, present detailed study comparing their output on the same speech input. The selected example corresponds to female speaker expressing the line: "Okay, I'm sorry, but just stood in this line for hour. Can I-is there any way can The ground truth emotion label for this utterance is sad.
As shown in Table other representative SpecchLLMs in this example. The models include Kimi-Audio (KimiTeam et al., 2025), Audiu---Adoo-IIttueee (Chu et al., 2024), Qwen2.550mmni-7 (Xu al., 2025b), BLSO- Emo (Wang et al., 2024), Phi-4-Multimodal (Abouelenin et al., 2025), MERaLiON2 (He et al., 2025), DIVA (Held et al., 2024), Megrez-3B-OOn Team, 2024). GLM-4-Voice (?), and OSUM-EChat (Geng et al., 2025). The case study reveals that Emotion Thinker demonstrates more accurate and comprehensive capture of acoustic information, along with stronger logical consistency in emotion reasoning.
evaluate and compare the reasoning outputs EmotionTTinker with 11
(Infinigence AI, 2024), MiniCPM-O (MiniCPM-
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Prompt Template (Generate reward model training data)
You are an expert in emotional speech and vocal analysis. The given audio sample with the following cues:
groundtruth_ _erttitnnmmtnnno "fitem["majj major_ "majjrr_mmtionn]"""
transcription' ""tranncrippionn"
speaker_genders "iitem"gender_ne"" new"]]"
speaker_ags "'ag _[evell]]""
pitch_lever "(item[pitch_ "evvel"]"""""""
energy_level "(itemm"enerrgy__e [evee!""
speed_level", level" "(item["speed leve!]]"
ioonnation_cottull
stressed "oorssed_word" [itemm"stress"))
*Suppose have listened to the audio sample**. Your task is to provide *argget quality
level** reasoning explanation of *"why** the speaker is likely expressing the emotion labeled as (item["major emotion"]]" ([tem[majorremotiirr]]""
When you generate certain quality score reasoning content, you should follow quality rules below (1-5 score range):
#### *Facctul Alignment** How accurately the explanation incorporates the provided metadata (e.g., pitch, stress, transcription).
***5** Perfect use all relevant metadata with no factual errors. Deep integration of acoustic cues.
***"*;
Mostly accurate usage of metadata with minor omissions or inaccuracies.
**3**;
Partial use metadata; includes some mistakes or superficial mentions.
2
Sparse and incorrect use of metadata; core cues are missing or misused. misrepresents the provided metadata entirely.
1xx Fails
### **Interpretative Quality**
How logically and persuasively the reasoning connects acoustic linguistic features emotional interpretation (regardless factual correctness).
-**5**; Clear, logical, compelling reasoning; draws insightful connections between prosody and emotion.
*****; Mostly strong and logical interpretation, though some reasoning may be underdeveloped.
3** Reasoning is present shallow or formulaic; some connections are unclear.
2** Weak interpretative logic; vague or unconvincing justification.
1
No meaningful reasoning; assertion without explanation.
Figure 6: Prompt template for generating reward model training data (Part 1).
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Table 8: Case study comparing emotion reasoning outputs from EimotionThinker and 12 representative SpeechLLMs on the same audio sample. The ground-truth label is sad, and the analysis highlights differences in prosodic cue recognition, semantic integration, and logical coherence across models. EmotionThinker demonstrates more accurate and comprehensive capture of acoustic information, together with stronger logical consistency in its emotion reasoning.
Model
Reasoning
Ground Truth
The speaker's ' tone suggests emotional state that feels subdued or resigned. The pitch level is notably low, which often conveys a sense of heaviness dejection, especially in contrast to more dynamic or elevated pitch patterns associated with excitement or anger. This low pitch, combined with the normal energy level, implies the speaker isn't entirely drained but lacks enthusiasm vigor, a hallmark of someone carrying weight of sadness or disappointment. The transcription offers further insight. The line "Okay, I'm sorry, but I just stood in this line for an hour" carries an apologetic tone, which is reinforced by the word "sorry." Apologies, when coupled with low pitch and steady, normal pacing, often signal emotional vulnerability or regret. The stress on the word "hour" suggests the speaker is emphasizing the duration of their wait, likely reflecting a sense of frustration or resignation about wasted time. However, this frustration doesn't 't come across as angry; instead, the calm pacing and lack of heightened energy suggest quieter, more reflective emotional state. The speaker's profile as female teenager might also contribute to the interpretation, as younger individuals in such situations may express disappointment in less confrontational and more uncertain manner. Overall, the delivery aligns with an emotion rooted in sadness or subdued frustration.
Emotion' I hinker (ours)
The speaker, a teenage female, conveys sense of emotional tension through several prosodic and acoustic cues. The pitch level is notably low, which often signals a subdued restrained emotional state, potentially indicative of frustration or resignation. This is further supported by the low energy level, which suggests the speaker not exerting much vocal effort, a common trait when someone feels drained or exasperated. The speech rate is normal, which rules out heightened urgency or panic, but the stress placed on the word "hour" stands out. By emphasizing this word, the speaker draws attention to the duration of their experience, which likely feels burdensome or unfair to them. This stress pattern aligns with the semantic content of the transcription, where the speaker mentions standing in a line for extended period: "I just stood in this line for an hour." The phrasing itself hints at dissatisfaction or impatience, reinforcing the emotional tone. While the intonation contour is uncertain, the overall delivery relivery-marked by low pitch and energy -uggests a lack of enthusiasm or patience. Taken together, these prosodic features, combined with the semantic context, strongly point to an emotional state of frustration or irritation.
Qwen2-Audio-
Based on the speaker's s tone, intonation, and word choice, the speaker seems to be expressing a sense of confusion or perplexity. The use of the word 'can' (to the end) suggests that the speaker is trying to understand or clarify something, but the question mark at the end implies that they unsure about the answer. Additionally, the speaker's 's voice carries light and high-pitched quality, which often corresponds with feelings of confusion bewilderment. Overall, the combination of these factors leads me to believe that the speaker is feeling confused.
Instruct (Chu et al.,
2024)
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Qwen2.5-Ommi-73 (Xu al., 2025b)
The speaker is expressing neutral emotion in this audio clip. Reasoning: 1. Transcription: The speaker says, "Hi, I need an ID." " This is neutral statement, neither positive nor negative, indicating a straightforward request or instruction. 2. Prosody: The tone of the voice is even and lacks emotional inflection. There is no rise or fall in pitch that would suggest excitement, sadness, anger. Context: The phrase "I need ID" is common, everyday request that does not evoke strong emotions. It is a neutral, matter-of-facct statement, 4. Speaker Traits: The speaker's 's tone is steady and unemotional, which aligns with neutral emotional state. In summary, the speaker's neutral emotion is evident from the straightforward nature of the request and the even tone of voice.
BLSP-EMO (Wang et al.,
The emotion tone of the speech provided is sad. The speaker says they waited in line for an hour, which shows they are unhappy with the situation. Even though they start with "I'm sorry," their tone suggests they are annoyed and want solution quickly. The incomplete sentence at the end ("can I... is there any way I can- ") shows they are struggling to stay polite while also trying to get help. These signs together reflect sadness.
2024)
Phi-4- Multimodal (Abouelenin et al., 2025)
The speaker is expressing anger in this audio clip. The reasoning behind this is based on the prosody cues and the semantic content of the speech. The speaker's 's pitch is high, which is a common feature of anger. The speech speed is also fast, which can indicate frustration or irritation. The energy of the speech is high, which can also be associated with anger. The stress certain words or phrases can also indicate anger, the speaker may be emphasizing their point or expressing their frustration. The semantic content of the speech also supports the inference of anger. The speaker is expressing their frustration and annoyance at having to wait in line for an hour. They are also asking if there is any way they can be helped, which can indicate sense of helplessness or frustration. Overall, the combination of prosody cues and semantic content in the speech supports the inference that the speaker is expressing anger. The high pitch, fast speed, high energy, and stressed words all contribute to this inference. The semantic content of the speech also supports the inference of anger, as the speaker is expressing frustration and annoyance having wait in line for hour.
MERaLiON2 (He et al.,
'The speaker is expressing frustration. Here's 's why: Prosody Cues: The speaker's 's tone slightly elevated, with slightly faster pace and noticeable increase in energy when they say "I just stood in this line for an hour." " This indicates agitation and impatience. & Semantic Cues: The phrase "I just stood in line for an hour" conveys sense of wasted time and frustration. The speaker is clearly unhappy about the situation. Context: The speaker is likely addressing a customer service represen- tative or someone in authority, as evidenced by the phrase "is there any way that I can..." This suggests desire for solution to their problem, further emphasizing their frustration. While the speaker apologizes at the beginning, the overall tone and content of their statement point towards frustration rather than sadness or anger..
2025)
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DIVA (Held et al., 2024)
Based on the audio clip, would argue that the speaker is expressing frustration. Here's my reasoning: 1. Prosody cues: The speaker's 's tone is flat and monotone, with a slightly rising pitch at the end, which suggests sense of resignation and frustration. The pace is moderate, but the speaker's 's words are delivered in slow and deliberate manner, which adds the sense of frustration. 2. Semantic cues: The speaker's words convey a sense of disappointment and annoyance. The phrase "I stood here for an hour" implies a significant amount of time wasted, and the speaker's 's tone suggests that they are not pleased about it. 3. Speaker traits: The speaker's 's age and gender are not explicitly mentioned, but the tone and language used suggest young adult or an older teenager. The speaker's s frustration is likely due to a sense of injustice or unfairness, which is common emotion among young people. 4. Contextual cues: The speaker is expressing their frustration about waiting in line for an hour, which is relatable and common experience. The speaker's 's frus- tration is likely amplified by the fact that they feel they have wasted a significant amount of time. Overall, the speaker's i tone, language, and context all suggest that they are expressing frustration and disappoint- ment about the situation.
Megrez-3B- 3Bonmni (Intini-
The speaker in this audio clip appears to be expressing a sad emotion. Here's a comprehensive reasoning of why this is the case: 1. Prosody Cues: The speaker's intonation is flat and monotonous, which often indicates a lack of enthusiasm or emotional depth. This can be associated with sadness disappointment. 2. Semantic Cues: The phrase "T m sorry but just stood in this line for an hour" suggests that the speaker has experienced frustrating disappointing situation. The use of the word "sorry" indicates a sense of regret or remorse, which can be a sign of sadness. 3. Contextual Clues: The fact that the speaker has been standing in a line for an hour suggests that they have been waiting for something or someone, which can be source of frustration and disappointment. This context, combined with the emotional tone of the speech, supports the interpretation of sadness. Age Level: While gender and age level are not definitive indicators of emotion, they can sometimes provide additional context. In this case, the speaker's gender and age level are not specified, so they do not significantly influence the interpretation of the emotion. 5. Overall Tone: The overall tone of the speech is subdued and lacks energy, which is often associated with sadness or disappointment. In conclusion, the combination of prosody cues, semantic cues, contextual clues, and the overall tone of the speech suggests that the speaker is expressing a sad emotion.
gence AI, 2024)
Gender and
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MiniCPM-O (MiniCPM- Team, 2024)
The emotion expressed in this audio clip is contempt. Here's 's a compre- hensive analysis: 1. Speaker Traits: The speaker appears to be female, given the voice characteristics and intonation patterns typically asso- ciated with women. There are significant age indicators from the provided information; however, her tone suggests she might be an adult or young adult. 2. Prosody Cues: Intonation: The pitch of the voice fluctuates slightly but generally remains level without much variation, which can indicate lack of enthusiasm engagement. This supports the idea that the speaker does not feel strongly about what they re say- ing. Speed: Her speech speed seems normal, neither particularly fast nor slow, suggesting neutral pace rather than one indicative of strong emotions like excitement (fast) or frustration (slow). Energy: The energy conveyed through her voice is low-key, lacking high levels of volume or intensity often found in expressions of anger or happiness. 3. Semantic Cues: Transcription Analysis: "Okay, I'm sorry, but just stood in this line for an hour" indicates a situation where somcone has waited for long time, likely leading to feelings of annoyance dissatisfaction. However, the phrase "is there any way that I..." shows politeness and desire for resolution, hinting underlying frustration tempered by courtesy. Combining these factors leads us to conclude that the primary emotion being expressed is contempt--speccfically specifically, mild disdain or irritation towards having wasted time waiting in line.
GLM-4- Voice al., 2024)
(Zeng
In this audio clip, the speaker seems to express exhaustion and possibly frustration. The words "I' m sorry but I just did this for an hour" suggest that they's re expressing sense of relief or weariness, possibly because they've been doing the same task for long time. The tone of their voice, slow and monotonous, matches the physical exhaustion they describe, further supporting the idea that they're feeling tired and perhaps bit annoyed
OSUM-EChat et al., 2025)
(Geng
The emotion expressed in this audio clip is neutral. The speaker is responding in calm, composed manner. There's no obvious tone of anger, sadness, fear, or surprise. They maintain a polite and even delivery ("T m sorry but just stood in line for an hour... Can there be any way that"). The speaker could be regarded as polite and resigned, which generally aligns with neutral state they are not conveying strong emotions like joy (happiness) or distress (sadness).
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Prompt Template (Generate reward model training data)
#### 3. **Caption Completeness**
How thoroughly the explanation covers all relevant metadata and contextual cues (regardless of factual accuracy).
**5" Comprehensive coverage of nearly all prosodic and semantic cues.
* Covers most key elements, though 1-2 important cues may be missed.
"3" Mentions several cues but misses critical aspects or remains surface-level.
*2"" Very limited coverage; ignores most important metadata.
*11 *** Only touches single cue or entirely incomplete.
#### 4. **Fluency & Structure Clarity** How How well-structured, fluent, and easy-to-follov the explanation is (ignoring content accuracy).
* Very clear, coherent, and well-written; excellent structure and flow.
= Mostly fluent and understandable; minor grammatical or structural issues.
3
may inconsistent. Hard follow; poorly organized or expressed.
Some awkward unclear parts;
****; Incoherent or barely readable.
### "*Requiremets:***
-You **must** write the explanation as you had **only listened to the audio and **did
not access the metadata.
Like human listener, use auditory reasoning to deduce the likely emotion, referencing
related speaker traits, prosody cues, and (emantic(tannscrip)) cues.
Assume you **not** know ground-truth label and suppose you listened this audio. Like a human listener, infer the most likely emotion through step-by-step reasoning. Let the conclusion arise naturally from your reasoning process. The reasoning process focus should **acoustic prosodic cues**, while semantic content may reference.
secondary
* *Imprrtnnt** Your explanation rellettt the given score quality exactly:
Factual Alignment** score: (item('factural alignment
aalignnte score']
Interpreetative Quality"* score: ineemiinterpretative _twllltuatss_aaeeeeee score']]
Capttio Completeness** score: caption completness "ocplletess_sccr__]]__
**lariity & Coherence**
ittem//darity.cchherennsss
cohenrences "ccrre"
The reasoning must be beeteen 50-200 *
*I the target overall quality is low**, you may generate **shorter and less detailed** explanations (closer to 50-100 words).
**If the overall quality is high** aim for **more complete and thorough explanation** (closer 200 words). Adapt the length "*proportionally to the overall quality** while staying within 50-200 words range.
Only return the emotional reasoning content.
Figure 7: Prompt template for generating reward model training data (Part 2).
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Prompt Template (Emotion reasoning evaluation)
You expert evaluator emotion recognition reasoning quality. Your evaluate godel-generated explanation (within <thinke...</thinn for quality justifying the predicted emotion label (within <answers.. </answerr), using provided metadata and reference explanation.
Evaluate the explanation across ""two dimensions** on **1-5 scale**:
*55*
Better than the reference.
Good as reference in quality and grounding, allow with few factual mismatch.
**3**: Weak reasoning, structure many factual errors (Note: if have multiple factual
should only affect Factual Alignment score should influence other metrics.)
***2**
Totally irrelevant, serious hallucinated, meaningful reasoning
****=x;
Very bad reasoning process.
#*# Context Information
Metadata of the audio:**
groundtruth eemotion ""fitem"majjrr_eme" "mmotion""m
transcription' "
speaker Boaiierr_eeerrrrre "eneder"""jeemm"ggndde" "'item["gender""""
speaker age" ""aittmmaageellleell"" [
pitch_ Micch_leeel:: fittem[pitchhlevell"
energy_level',, "'jitem["energ""" leanenny_eeeeelllll level])"
speed level": : "(itemm[speed_
intonation_contour contour' ""fitem[intonnation "itpnttnnatiooccnnourr""""""""
stressed word". "(item["stress"]
*Reference Explanation:** reeesoig_reassnnng]]]
-eeeeel-eeeerated Explanation (to evaluate):*** preer"onnn__
#1## Evaluation Dimensions
1. **Factual Alignment**
How accurately the explanation uses the provided metadata (e.g., pitch, stress, transcription).
Score high correctly references facts; penalize hallucinations or omissions.
Figure Prompt template for emotion reasoning evaluation (Part 1).
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Prompt Template (Emotion reasoning evaluation)
You expert evaluator emotion recognition reasoning quality. Your evaluate model-generated explanation (within <think>... </thinnkk) for quality justifying the predicted emotion label (within <answer>... </answer>), using provided metadata and reference explanation.
### Evaluation Dimensions
1. **aactual Alignment**
How accurately the explanation the provided metadata (e.g., pitch, stress, transcription).
Score high if it correctly references facts; penalize hallucinstions omissions.
2. *#Interpretative Quality**
Only focus interpretative. How logically and persuasively the explanation connects acoustic and linguistic features the emotion. Score high for clear, structured, and convincing reasoning.
3. **Caption Completeness**
Only focus on overall caption, ignore weather have factual inaccuracies. Whether explanation covers key metadata points and contextual cues relevant to the emotion. ignored.
Reward comprehensive coverage; penalize if important
4. *Clarrity Coherence**
Only focus overall coherence. How well-written and easy-to-follow the explanation is. Consider fluency, structure, and conciseness.
НiН Output Format
Only return JSON object scores concise comment:
"json
EpactualAllgnmenn int, "Interpretative. Lattrree_aaaattttt int,
"Caption_ "Caption_Completenee" Completeness": int,
"Clarity_ Conerrecce" int,
"comment": "Brief rationale for scores (2-3 sentences)."
Figure Prompt template for emotion reasoning evaluation (Part 2).
27


Reviews:
[{"review_id": "yx3c2VnZgw", "reviewer": "Reviewer_Ez3y", "summary": "The paper explores an interesting problem by extending emotion modeling from classification to reasoning with promising results. However, the methodology and training details are unclear, reproducibility is lacking due to missing code release, and definitions of prosody and emotional cues need stronger justification and clarity.", "questions": "How do you handle emotional cues that arise from linguistic content? For example, if the text conveys a happy emotion but the corresponding speech expresses sadness, which modality is prioritized in your final emotion prediction? Does emotion inferred from text affect the overall performance of your model?\n\nPlease provide both theoretical justification and experimental evidence to support your claim that “prosodic signals are core carriers of emotional intent.” How do you account for the role of textual content and nonvocal components (e.g., crying, laughter)? If you argue that prosody is the most dominant factor, please include empirical evidence demonstrating that prosody contributes more significantly to emotion perception than textual and nonvocal cues.\n\nPlease clarify how you define prosody. Are speaker traits such as gender and age also included under this term? Appropriate literature references should be provided to accurately define both “prosody” and “speaker traits,” as some of the current definitions appear to be inaccurate.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "The motivation is clear, and the research problem is interesting, as it extends beyond improving emotion classification toward developing deeper reasoning capabilities.\n\nThe proposed model demonstrates strong performance in both emotion recognition and emotion reasoning, providing valuable insights for advancing SpeechLLMs toward more effective emotion reasoning capabilities.", "weaknesses": "It is unclear how your model is trained and how it builds upon Qwen2.5-Omni-3B. Please clarify the training process and provide clear explanations for all symbols and notations in your equations, as they are currently difficult to interpret.\n\nThe methodology section, particularly Section 3.3, lacks clarity. Please provide a clear description of the overall training pipeline and explain the motivation behind each step. The writing in Section 3.3.1 should be further improved for better structure and readability. Additionally, clarify the purpose of the forward reward and outcome accuracy reward, why both are needed, how they relate to the components shown in Figure 3, and what their specific inputs and outputs are.\n\nWill you release your code and dataset? The reproducibility checklist is missing, and without a clear commitment to open-sourcing these resources, the paper’s reproducibility and credibility are severely limited. I may not be able to recommend acceptance unless this issue is properly addressed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "c4EsrWdCYz", "reviewer": "Reviewer_uQyK", "summary": "First, I would like to summarize the contributions of the work by reading the abstract.\n\nFirst, a speech emotional dataset was constructed. Second, current speech LLMs have weak prosody perception. This work tries to address this issue by developing a prosody-enhanced foundation model. Third, a new type of reinforcement learning protocol is proposed, which progressively introduces a reasoning award by dynamically adjusting it with trustworthy weights, reflecting the alignment between reasoning and outcome.", "questions": "**Other comments:**\n\nFor section 3.1, the authors discussed the open-sourced datasets they used to construct the EmotionCot-35k.  I think authors could have a brief discussion on other related, multimodal datasets in the paper as well, whether to use them for constructing the new dataset or not. Such as the following:\n\nMSP-PODCAST (the most recent, final version): Busso, Carlos, et al. \"The msp-podcast corpus.\" arXiv preprint arXiv:2509.09791 (2025).\n\nMERSA: Zhang, Enshi, Rafael Trujillo, and Christian Poellabauer. \"The MERSA dataset and a transformer-based approach for speech emotion recognition.\" Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.\n\nCMU-MOSEI: Zadeh, AmirAli Bagher, et al. \"Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph.\" Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.\n\nDECAF: Abadi, Mojtaba Khomami, et al. \"DECAF: MEG-based multimodal database for decoding affective physiological responses.\" IEEE Transactions on Affective Computing 6.3 (2015): 209-222.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "**1.** The motivation of the work is clearly stated and explained.\n\n**2.** A first RL-based emotion recognition that has the ability not only for accurate classification, but detailed reasoning rationales and informative captions for the audio.\n\n**3.** Each stage of the proposed framework is clearly defined.\n\n**4.** The evaluation and abolition are comprehensive.", "weaknesses": "**1.** For the accuracy of emotion recognition, I would also like to know the performance on each individual discrete emotion. That way, we can have a more concrete and detailed understanding of the framework's capabilities and limitations.\n\n**2.** To construct the reasoning responses, is there a specific reason that only GPT 4.0 is used?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "07aTLosbb2", "reviewer": "Reviewer_dYpE", "summary": "This paper proposes EmotionThinker, a novel framework for explainable speech emotion recognition (SER) that leverages CoT reasoning and reinforcement learning (RL) to move beyond standard categorical classification. The authors introduce EmotionCoT-35K, a large dataset with CoT annotations and fine-grained prosodic and semantic factors tailored for emotion reasoning. They further proposed an RL-based optimization framework (GRPO-PTR) that incorporates a progressive, trust-aware reasoning reward, balancing outcome accuracy and reasoning quality. Extensive experiments over four benchmarks and ablation studies demonstrate that EmotionThinker achieves superior emotion recognition accuracy and richer, more interpretable explanations compared to a wide range of baselines.", "questions": "1. Can the authors provide more analysis comparing the similarities and differences between GPT-4o-based and human-based scoring for CoT data and reasoning reward trace quality? Are there specific failure modes or biases in the model-synthesized data?\n2. Are there statistics on annotation accuracy for the automated annotations (prosody, stress, speaker traits) used in EmotionCoT-35K? Do certain emotion categories or speaker groups have systematically noisier annotations or explanations?\n3. Typos: Line 270, Appendix without reference.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The reformulation of SER as a deep reasoning task—rather than mere label prediction—is timely and promising for advancing interpretability in multimodal LLMs.\n2. The proposed dataset, EmotionCoT-35K, fills a significant gap with CoT-style, prosody-aware emotion reasoning data, with a scalable, largely automated annotation pipeline. This may have value for the broader community.\n3. The proposed reinforcement learning scheme employs progressive reward scheduling and a trustworthiness weight to dynamically balance outcome and reasoning reward signals. This helps mitigate reward hacking and stabilizes training and may be meaningful for the LLM RL community.", "weaknesses": "1. The data construction pipeline heavily relies on LLMs, and the reasoning trace data is constructed with GPT4o without the actual speech input. This may lead to unexpected failure and bias in the dataset. It would also be beneficial to input the speech and conduct a human review of the data quality.\n2. The proposed reward model plays a critical role in the RL process. However, there is little discussion or quantitative validation of its calibration. The distributions of GPT-annotated versus human-annotated reward scores are not directly compared.\n3. The description of the RL part is not very easy to follow. It would be better to improve the logit flow in this part.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YaLYVzPUgK", "reviewer": "Reviewer_tjXC", "summary": "This paper introduces **EmotionThinker**, a novel model for speech emotion reasoning that aims to reframe Speech Emotion Recognition (SER) from a simple classification task into a deep, explainable reasoning problem. The core contribution is the design of a **Prosody-Aware Reinforcement Learning (RL) framework**. This framework guides a Large Language Model (LLM) to generate coherent, feature-grounded text explanations (i.e., reasoning paths), thereby bridging raw acoustic signals, the textual reasoning process, and the final emotional label. This innovative approach addresses the critical lack of interpretability in existing SER systems and SpeechLLMs.", "questions": "1. RL Reward Faithfulness and Ablation (Critical)\n\n\tProvide an **Ablation Study** on the RL reward components. How do you ensure the explanations are **truly faithful to prosodic facts** and not just syntactically fluent fabrications?\n\n2. Technical Mechanism of Prosody Grounding\n\n\tClarify the **explicit mechanism** that links a generated text token (e.g., \"high pitch\") to the **specific, salient acoustic feature** in the speech input.\n\n3. Practicality and Efficiency Analysis\n\n\tProvide detailed **Inference Latency** and **Training Cost (GPU-hours)** analysis. Is this model practically deployable compared to lightweight SER baselines?\n\n4. Generalization\n\n\tShow **Out-of-Distribution (OOD)** results to prove that the learned reasoning generalizes beyond the training corpus.", "limitations": "", "rating": 8, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Originality (Originality):** Very High. The combination of SER, LLM, and RL specifically tailored for generating prosody-grounded explanations is highly novel within the speech community.\n- **Quality (Quality):** High. The approach moves beyond simple performance metrics by incorporating explanation quality into the optimization objective, indicating a rigorous research focus on a complex problem.\n- **Clarity (Clarity):** Good. The core idea is presented clearly, and the reasoning process (via case studies) is easily digestible.\n- **Significance (Significance):** Substantial. This work significantly pushes the boundaries of transparency and trust in SpeechLLMs for emotional tasks, which is an important step for the future of multimodal AI.", "weaknesses": "1. **Technical Granularity of RL and Prosody Integration (Critical):**\n\t- The paper emphasizes **\"Prosody-Awareness,\"** but the precise and **explicit mechanism** by which the RL framework guides the LLM to attend to the *most critical* prosodic features (e.g., sudden pitch shifts over average pitch) needs more profound elaboration.\n\t- The **RL Reward Function** is paramount. A detailed ablation study is essential to show how the different components of the reward (classification accuracy vs. fluency vs. factual grounding to acoustic features) are balanced and how this balance impacts the quality and faithfulness of the final explanation. The current description suggests this crucial balance may be underspecified.\n2. **Data and Generalization Concerns:**\n\t- Training LLMs via RL for generation tasks often relies heavily on high-quality **human-annotated reasoning path data** for initial Supervised Fine-Tuning (SFT) or as part of the reward signal. The paper must provide a candid discussion on the cost and scarcity of this data and how the model manages to generalize its reasoning to novel or atypical speech examples outside the training distribution.\n\t- Generalization to different languages or accents, crucial for a model involving LLM-style reasoning, is also a concern that needs addressing.\n3. **Efficiency and Deployment Feasibility (Practicality):**\n\t- The combination of LLM and RL training typically incurs a substantial computational overhead. The paper is currently lacking in a detailed analysis of the **training efficiency, required computational resources (GPU-hours)**, and most importantly, the **inference latency** compared to existing, lightweight SER systems. This is vital for assessing the model's practical viability for real-world deployment.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Oral)