EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions

## Abstract

Abstract GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models.
However, empowering Large Language Models to perceive and generate images, texts, and speeches end-to-end with publicly available data remains challenging in the open-source community.
Existing vision-language models rely on external tools for the speech processing, while speech-language models still suffer from limited or even without vision-understanding abilities.
To address this gap, we propose EMOVA ( EM -otionally O mni-present V oice A ssistant), to enable Large Language Models
with end-to-end speech capabilities while maintaining the leading vision-language performance.
With a semantic-acoustic disentangled speech tokenizer, we notice surprisingly that omni-modal alignment can further enhance vision-language and speech abilities compared with the corresponding bi-modal aligned counterparts.
Moreover, a lightweight style module is proposed for flexible speech style controls ( e.g . , emotions and pitches).
For the first time, EMOVA achieves state-of-the-art performance on both the vision-language and speech benchmarks, and meanwhile, supporting omni-modal spoken dialogue with vivid emotions.

## 1 Introduction

OpenAI GPT-4o (OpenAI, ), a new milestone for omni-modal foundation models, has rekindled people’s attentions on intelligent assistants that can see (i.e., perceiving fine-grained visual inputs), hear (i.e., understanding vocal instructions) and speak (i.e., generating vocal responses) simultaneously.
Most existing Multi-modal Large Language Models (MLLMs) focus on two modalities only, either vision-language (Bai et al., ; Li et al., ) or speech-language (Chu et al., ; Xie & Wu, ), demonstrating severe demands for omni-modal models with visual, language and speech abilities.
How to effectively empower Large Language Models (LLMs) to process omni-modal data in an end-to-end manner remains an open question.

Existing omni-modal LLMs (Chen et al., ; Fu et al., ) generally build upon Vision LLMs and integrate the speech modality by adopting a speech encoder like Whisper (Radford et al., ), which extracts continuous features from speech, similar to how images are processed, and enables speech understanding.
However, these models still rely on external Text-to-Speech (TTS) tools for generating speech responses, limiting their ability to support real-time interactions.
AnyGPT (Zhan et al., ), instead, opts for a fully discretization manner, which first discretizes all data modalities (i.e., images, texts, and speeches), followed by omni-modal auto-regressive modeling.
This enables AnyGPT to handle multiple modalities with a unified end-to-end framework, facilitating real-time interactions with the help of streaming decoding.
However, the discrete vision tokenizer adopted by AnyGPT struggles to capture visual details, especially for high-resolution images, making it far behind its continuous counterparts on vision-language benchmarks.
Moreover, none of the existing works explore speech style controls (e.g., emotions and pitches) with LLMs.
Therefore, our question arises: How to build an end-to-end omni-modal LLM enabling spoken dialogue with vivid emotions while maintaining state-of-the-art vision-language performance?

In this paper, we propose EMOVA (EMotionally Omni-present Voice Assistant), a novel end-to-end omni-modal LLM with state-of-the-art vision-language and speech capabilities while supporting emotional spoken dialogue.
Fig. 1 shows an overview of the model framework.
A continuous vision encoder captures the fine-grained visual details, while the discrete speech tokenizer and detokenizer enable the end-to-end speech understanding and generation.
Specifically, the speech-to-unit (S2U) tokenizer converts the input speech waveforms into discrete speech units as LLM inputs, while the unit-to-speech (U2S) detokenizer reconstructs the speech waveforms from the LLM’s output speech units.
To seamlessly integrate the speech modality with LLMs, we meticulously design a semantic-acoustic disentangled speech tokenizer to decouple the semantic contents and acoustic styles of the input speeches (Tao et al., ), where 1) semantic content (i.e., what it says) captures the semantic meanings of input speeches, which is finally discretized and aligned with LLMs, while
2) acoustic style (i.e., how it says) captures the diverse speech styles (e.g., emotions and pitches).
Utilizing the semantic-acoustic disentanglement of our speech tokenizer, we further introduce a lightweight style module to support spoken dialogue with vivid emotions and pitches.
As in Sec. 4.1, this innovative disentanglement design better facilitates the modality alignment between texts and speeches while maintaining flexibility for diverse speech style controllability and personalization.

With the end-to-end omni-modal architecture of EMOVA, we empirically demonstrate that publicly available bi-modal image-text and speech-text data are sufficient for omni-modal alignment, utilizing the text modality as a bridge. This eliminates the need for omni-modal data (i.e., image-text-speech), which is usually scarce. Surprisingly, we find that omni-modal alignment can further improve both vision-language and speech capabilities through joint optimization, even when compared with their bi-modal aligned counterparts.
Finally, only a small amount of mixed-modality samples are required to teach the model to respond in the desired format.
For the first time, EMOVA achieves state-of-the-art performance on both vision-language and speech benchmarks (see Table 1 for comparisons).
The main contributions of this work contain three parts:

Figure: Figure 1: Model architecture of EMOVA. The vision encoder extracts continuous visual features, which are projected into the text embedding space as visual tokens, while the input speech is encoded and quantized into discrete units. Given the omni-modal inputs, EMOVA can generate both textual and speech responses with vivid emotional controls. Check Sec. 3 for more architectural details.
Refer to caption: x1.png

- 1.
We propose EMOVA, a novel end-to-end omni-modal LLM that can see, hear and speak. We use a continuous vision encoder and a semantic-acoustic disentangled speech tokenizer for seamless omni-modal alignment and diverse speech style controllability.
- 2.
We introduce an efficient text-centric omni-modal alignment which can further improve the vision-language and speech capabilities, even compared with the corresponding bi-modal aligned counterparts (i.e., image-text only and speech-text only alignment).
- 3.
For the first time, our EMOVA achieve state-of-the-art comparable performance on both the vision-language and speech benchmarks simultaneously, while supporting flexible spoken dialogues with vivid emotions.

**Table 1: Comparison among Multi-modal Large Language Models. Our EMOVA is the very first unified Omni-modal Large Language Model capable of emotional spoken dialogue with state-of-the-art vision-language and speech capabilities simultaneously.**
|  | Method | Visual | Text | Speech |  |  |
| --- | --- | --- | --- | --- | --- | --- |
|  | Understand | Generation | Emotional |  |  |  |
| Vision | LLaVA | ✓ | ✓ | ✗ | ✗ | ✗ |
| Intern-VL | ✓ | ✓ | ✗ | ✗ | ✗ |  |
| Speech | Qwen-Audio | ✗ | ✓ | ✓ | ✗ | ✗ |
| Mini-Omni | ✗ | ✓ | ✓ | ✓ | ✗ |  |
| LLaMA-Omni | ✗ | ✓ | ✓ | ✓ | ✗ |  |
|  | Intern-Omni | ✓ | ✓ | ✓ | ✗ | ✗ |
|  | VITA | ✓ | ✓ | ✓ | ✗ | ✗ |
|  | Any-GPT | ✓ | ✓ | ✓ | ✓ | ✗ |
| Omni | EMOVA (ours) | ✓ | ✓ | ✓ | ✓ | ✓ |

## 2 Related Work

#### Vision Large Language Models

(VLLMs) integrate vision modality into Large Language Models (LLMs) (Touvron et al., ; Chen et al., ), enabling advanced understanding and reasoning over visual instructions (Liu et al., ; Bai et al., ; Gou et al., ; ).
Recent efforts in VLLMs can be broadly categorized into three directions, including 1) Vision encoders (Oquab et al., ; Chen et al., ; ) are enhanced and aggregated for robust representations (Lin et al., ; Li et al., ; Tong et al., ).
2) High-resolution methods are proposed to overcome the fixed resolution of pre-trained vision encoders (e.g., $336\times 336$ for CLIP (Radford et al., )), empowering LLMs to perceive fine-grained visual information (Liu et al., ; Dong et al., ; Huang et al., ; Luo et al., ).
3) High-quality instruction data is essential for the VLLMs to generate accurate and well-formed responses following instructions (Laurençon et al., ; Li et al., ; Chen et al., ).
In this paper, besides achieving state-of-the-art vision-language performance, we further introduce speech understanding and generating capabilities into our EMOVA.

#### Speech Large Language Models

(SLLMs) empower speech interaction with LLMs.
Continuous SLLMs (Wu et al., ; Chu et al., ) utilize the speech encoders (Radford et al., ) to extract continuous speech embeddings for LLM, which, however, only support speech understanding, relying on external TTS modules for speech generation, and therefore, hampering real-time interaction.
Discrete SLLMs (Zhang et al., ), instead, first discretize speech signals with speech tokenizers, followed by auto-regressive modeling.
Recent works (Fang et al., ; Xie & Wu, ) further combine the continuous speech encoders with the discrete speech tokenizers for better performance.
Although effective, none of the existing works explore speech style controllability in SLLMs (e.g., genders, emotions, and pitches), which is essential for real-life spoken dialogue.

#### Omni-modal Large Language Models

support visual, text, and speech capabilities with a unified architecture simultaneously.
Similar to the continuous SLLMs, InternOmni (Chen et al., ) and VITA (Fu et al., ) connect a speech encoder with VLLMs, supporting speech understanding only.
Instead, AnyGPT (Zhan et al., ) proposes a unified architecture to discretize and conduct auto-regressive modeling for image, text, and audio simultaneously, which, however, suffers from inevitable information loss brought by discretization, especially for the high-resolution visual inputs.
In this work, we propose EMOVA, the very first unified Omni-modal Large Language Models with state-of-the-art vision-language and speech performance at the same time.

## 3 Architecture

### 3.1 Formulation

Denote the Large Language Model (LLM) as $f(\cdot)$ and the text, visual and speech inputs as $\textbf{X}_{T}$, $\textbf{X}_{V}$ and $\textbf{X}_{S}$, respectively.
$\textbf{X}_{T}$ is converted to discrete tokens $\textbf{U}_{T}$ via a text tokenizer (Gage, ), while the visual input $\textbf{X}_{V}$ is first encoded with a vision encoder $v(\cdot)$ as $\textbf{E}_{V}=v(\textbf{X}_{V})$, and then projected into the text embedding space with a projector $p(\cdot)$ as $\textbf{H}_{V}=p(\textbf{E}_{V})$.
As for the speech input $\textbf{X}_{S}$, a Speech-to-Unit (S2U) procedure is required.
Specifically, $\textbf{X}_{S}$ first passes through a speech encoder $s(\cdot)$ as $\textbf{E}_{S}=s(\textbf{X}_{S})$, which is then discretized by the quantizer $q(\cdot)$ as $\textbf{U}_{S}=q(\textbf{E}_{S})$.
The LLM $f(\cdot)$ is then trained to compute the joint probability of the output text and speech units $\textbf{U}_{T}^{o},\textbf{U}_{S}^{o}$ as

$$ $\mathbb{P}(\textbf{U}_{T}^{o},\textbf{U}_{S}^{o}|\textbf{U}_{T},\textbf{U}_{S} ,\textbf{H}_{V})=\prod_{i=1}^{L}\mathbb{P}(\bm{x}_{i}|\textbf{U}_{T,<i}^{o}, \textbf{U}_{S,<i}^{o},\textbf{U}_{T},\textbf{U}_{S},\textbf{H}_{V}),$ (1) $$

where $\bm{x}_{i}\in\textbf{U}_{T}^{o}\cup\textbf{U}_{S}^{o}$ and $L=|\textbf{U}_{T}^{o}|+|\textbf{U}_{S}^{o}|$.
The output response units $\textbf{U}_{S}^{o}$ are then recovered into the output speech waveform $\textbf{Y}_{S}^{o}$ via a Unit-to-Speech (U2S) decoder $d(\cdot,\cdot)$ together with an emotion style embedding $\textbf{E}_{style}^{o}$ to realize vivid emotional spoken dialogue controllability (Sec. 3.2).

#### LLM.

We adopt the LLaMA-3.1-8B (Dubey et al., ) as our base LLM $f(\cdot)$, due to its superior performance among publicly available checkpoints, which is equipped with a tiktoken text tokenizer and a vocabulary size of 128,256, supporting both multilingual textual inputs and outputs.

#### Vision encoder and projector.

We utilize InternViT-6B (Chen et al., ) as our visual encoder $v(\cdot)$ with $448\times 448$ base resolution, and C-Abstractor (Cha et al., ) with two ResBlocks (both before and after the pooling layer) and $4\times$ downsample rate as vision projector $p(\cdot)$.
To process the high-resolution inputs, the high-resolution image-slicing (Liu et al., ) is used, where visual tokens for one image are concatenation with a low-resolution thumbnail and the origin high-resolution image with separators in each line, allowing a maximum of nine tiles during training.

### 3.2 Speech Tokenization

#### Speech-to-unit (S2U) tokenizer.

Following Tao et al. (), we adopt the SPIRAL (Huang et al., ) architecture for the speech encoder $s(\cdot)$ to capture both phonetic and tonal information, which is then discretized by the quantizer $q(\cdot)$ utilizing the finite scalar quantization (FSQ)  (Mentzer et al., ).
The size of the speech codebook is 4,096, while the sample rate is 25 tokens per second.
Once discretized, the speech modality can be simply integrated into LLMs by concatenating the text vocabulary and speech codebook.

Our S2U tokenizer provides the following advantages:
1) Data efficiency: after pre-training on large-scale unlabeled speech data, it requires only a small amount of speech-text pair data for fine-tuning.
2) Bilingual: the speech codebook is shared among different languages (i.e., English and Chinese), sharing unit modeling abilities across languages.
Check more training details and comparisons with other speech tokenizers (Zhang et al., ) in Appendix A.1.

#### Semantic-acoustic disentanglement.

To seamlessly align speech units with the highly semantic embedding space of LLMs, we opt for decoupling the semantic contents and acoustic styles of input speeches.
Specifically, given input speechs $\textbf{X}_{S}$, both semantic embedding $\textbf{E}_{semantic}$ and style embeddings $\textbf{E}_{style}$ are extracted separately, while only the $\textbf{E}_{semantic}$ is quantified by $q(\cdot)$ to generate speech units $\textbf{U}_{S}$.
By changing $\textbf{E}_{style}$ while maintaining the same $\textbf{E}_{semantic}$, we can easily control speech styles without disturbing the semantic contents of recovered speeches.
Moreover, the disentanglement facilitates modality alignment among speeches and texts, as later shown in Sec. 4.1.

#### Unit-to-speech (U2S) detokenizer with style control.

Building on VITS (Kim et al., ), our U2S detokenizer adopts a conditional VAE architecture (see Fig. 9).
To achieve vivid style controls, we utilize the semantic-style disentanglement of our S2U tokenizer (as discussed above) and adopt a novel style embedding to control the speech styles (e.g., speaker identities, emotions, and pitches).
Specifically, the LLM $f(\cdot)$ is trained to generate both the output speech units $\textbf{U}_{S}^{o}$ and a style label.
The speech units $\textbf{U}_{S}^{o}$ are converted to unit embeddings $\textbf{E}_{semantic}^{o}$, while the style label is utilized to generate a unique style prototype $\textbf{E}_{style}^{o}$.
Both $\textbf{E}_{semantic}^{o}$ and $\textbf{E}_{style}^{o}$ are taken as inputs to speech decoder $d(\cdot,\cdot)$ to synthesize the output speech waveform $\textbf{Y}_{S}^{o}$.
See Appendix A.2 for more details.

Our U2S detokenizer is pre-trained on LibriTTS (Zen et al., ) and AISHELL-1 (Bu et al., ) and subsequently fine-tuned on synthetic style-rich speech data.
Specifically, due to the scarcity of real-life style-rich data, we utilize TTS tools (Du et al., ) to synthesize speech samples diverse in genders, pitches, and emotions.
As for style prototypes, Emotion2Vec (Ma et al., ) is adopted to select the most representative samples with the highest confidence in conveying the desired style.
Our empirical results reveal that even one representative style reference speech has been sufficient to control the speech styles flexibly and precisely.

## 4 Omni-modal Alignment and Instruction Tuning

To achieve the omni-model alignment, it is ideal to utilize large-scale omni-modal image-text-speech data, which, however, is either without reach due to copyright (Nagrani et al., ) or limited in the quality (Miech et al., ).
An alternative is to use existing image-text data with TTS-synthesized speeches, which is not only computationally expensive but also hampers data diversity, as most TTS tools generate speech in similar patterns.
Recent works (Chen et al., ; Fu et al., ) choose to integrate the speech modality into a well-structured VLLM via a sequential training manner with bi-modal alignment datasets.
However, the relationships between different modalities and how to effectively leverage multiple bi-modal alignment datasets remain an open question.

In this work, we explore omni-modal text-centric alignment by utilizing publicly available bi-modal alignment datasets, including both image-text (e.g., captioning) and speech-text (e.g., ASR and TTS) datasets.
With the text modality as a bridge, EMOVA ultimately becomes a unified system capable of understanding and generating multiple modalities in a coherent and integrated manner. Specifically, in Sec. 4.1, we explore the following three questions:

- 1.
Does the integration of the speech modality conflict with the vision-language capabilities?
- 2.
How to represent speech modality to foster omni-modal alignment?
- 3.
Is sequential alignment of multiple modalities optimal?

Then we introduce the omni-modal instruction tuning pipeline and the overall training paradigm of our EMOVA in Sec. 4.2 and Sec. 4.3, respectively.

Figure: Figure 2: Comparison between omni-modal alignment paradigms. 1) Joint training achieves consistent improvements over VL and Speech, suggesting omni-modal alignment can be beneficial across modalities. 2) Joint training outperforms both VL$\rightarrow$Speech and Speech$\rightarrow$VL, revealing that joint training is more superior and efficient than sequential training. 3) Joint is superior to Joint-entangled, highlighting the effectiveness of the semantic-acoustic disentanglement.
Refer to caption: x2.png

### 4.1 Omni-modal Text-Centric Alignment

#### Settings.

To answer the questions above,
we experimentally compare the following omni-modal training paradigms:
1) VL$\rightarrow$Speech conducts image-text alignment first followed by speech-unit-text alignment using the full speech data and 10% of image-text alignment data to avoid catastrophic forgetting, similar to InternOmni (Chen et al., ) and VITA (Fu et al., ).
2) Speech$\rightarrow$VL instead performs speech-unit-text alignment first and then aligns images with texts using 10% of the speech unit-text data and the full image-text data.
3) Joint aligns both modalities simultaneously. Note that unless otherwise specified, we use the S2U tokenizer introduced in Sec.3.2 to extract speech units for all speech data, which effectively disentangles semantic and acoustic features. Joint-entangled derives speech units using HuBERT (Hsu et al., ), which does not achieve semantic-acoustic disentanglement effectively with only Kmeans clustering.
4) VL and Speech only align the vision and speech modalities with texts, respectively, serving as baselines (see Appendix B.1 for more details).

#### Evaluation.

For speech capabilities, we directly evaluate the aligned model’s performance on the ASR task of LibriSpeech (Panayotov et al., ), while for vision-language, we fine-tune the model using a small amount of high-quality visual instruction data (i.e., the 665K SFT data from ShareGPT4V (Chen et al., )) and evaluate the fine-tuned model on common vision-language benchmarks.
Check Appendix C for evaluation details.
Fig. 2 shows the comparison among different paradigms on vision-language (left and middle) and ASR (right, where we report the $1-\text{WER}$ value for better readability) benchmarks, from which we can derive the following observations:

#### Observation 1: image-text and speech-unit-text data benefit each other.

Contrary to the common assumption that multiple modalities might compete and create conflicts, we find that introducing additional modalities is actually beneficial. As illustrated in Fig. 2, Joint consistently outperforms both VL and Speech across vision-language and speech benchmarks. Moreover, even models aligned sequentially, such as VL$\rightarrow$Speech and Speech$\rightarrow$VL, which are typically prone to catastrophic forgetting, demonstrate superior performance on most vision-language tasks. We speculate that the requirement to align multiple modalities with text leads to more robust representations, which in turn generalize better across different downstream tasks. This finding aligns with the results from ImageBind (Girdhar et al., ), where the joint alignment of audio and depth modalities with images resulted in improved downstream performance.

#### Observation 2: semantic-acoustic disentanglement benefits omni-modal alignment.

We find 1) Joint outperforms Joint-entangled on vision-language benchmarks, and 2) in the speech tasks, Joint maintains significant advantages over its entangled counterpart.
This can be attributed to the semantic-acoustic disentanglement which makes speech units more analogous to languages, a domain LLMs are particularly specialized.

#### Observation 3: sequential alignment is not optimal.

We notice that Joint consistently outperforms its sequential counterparts (i.e., VL$\rightarrow$Speech and Speech$\rightarrow$VL) on both vision-language and speech benchmarks, probably due to catastrophic forgetting when integrating a new modality.

In light of these observations, we have chosen to pursue the ultimate alignment strategy that simultaneously aligns image-text and speech-unit-text for EMOVA, which offers two important benefits, 1) it fosters mutual enhancement between vision-language and speech capabilities, and 2) it avoids the issue of catastrophic forgetting during sequential alignment of multiple modalities.

Figure: Figure 3: Demonstration of the omni-modal instruction tuning. 1) To empower emotional spoken dialogues, EMOVA is trained to explicitly select the speech style labels (e.g., emotions and pitches) with output speech units. 2) For the ease of parsing, data components are arranged in JSON format.
Refer to caption: x3.png

### 4.2 Omni-modal Instruction Tuning

After the omni-modal text-centric alignment, the model learns fundamental vision-language (e.g., captioning) and speech capabilities (e.g., ASR and TTS).
However, instruction tuning is essential to better follow complicated user instructions and respond with vivid emotions.

#### Emotion-enriched instruction data synthesis.

Due to the scarcity of omni-modal instruction data (i.e., dialogues involving images, speeches, and texts simultaneously), we opt for synthesizing omni-modal instruction data from existing text and visual instruction datasets.
First, we select instruction data suitable for the vocal expression by filtering out the non-vocal data (e.g., code and mathematical formulas).
Second, we clean the selected data to be more vocal by removing text formatting elements (e.g., $**$ and \n\n).
Then, we obtain style labels for the remaining dialog contexts, including genders (male, female), pitches (normal, low, high), and emotions (neutral, happy, sad, angry), resulting in $24$ different speech styles.
The style labels are obtained by prompting GPT-4o(^1^11https://chatgpt.ust.hk) to make reasonable inferences given the dialogue context.
Finally, we convert the textual instructions and responses into speeches using the latest TTS tools (i.e., CosyVoice (Du et al., ) and Azure AI Speech), and the style labels are used to control the style of the synthesized speech data.
To further improve the diversity of the data, each instruction is synthesized by randomly selecting one of the 39 available speakers.
Ultimately, we gather 120K speech-text and 110K speech-image data pairs in total. More details can be found in Appendix B.2.

#### Data organization and chain of modality.

The omni-modal instruction data can be represented as $D_{\text{omni}}=\{(x_{V},u_{S},x^{o}_{T},c^{o}_{\text{style}},u^{o}_{S})_{i}\}
_{i=1}^{N}$, where the input consists of the optional queried image $x_{V}$ and the speech units of the instruction $u_{S}$, while the output consists of the textual response $x^{o}_{T}$, the predicted speech style labels $c^{o}_{\text{style}}$, and the output speech unit $u^{o}_{S}$.
Note that we train EMOVA to explicitly select styles (e.g., emotions and pitches), which are utilized to determine the corresponding style embedding for the U2S detokenizer (Sec. 3.2).
Moreover, since directly generating the speech responses is challenging, we decompose the speech response procedure into three primary steps:
1) recognizing user instructions into texts;
2) generating textual responses based on the recognized instructions;
3) generating the style labels and response speech units based on the textual responses.
For ease of parsing during deployment, the target outputs are formatted as JSON, as shown in Fig. 3.

Figure: Figure 4: Overview of the data composition for EMOVA omni-modal instruction tuning. (Left) Distribution of instruction data across categories, with the outer circle representing overall categories and the inner circle depicting subset distributions. (Right) Quantitative breakdown of data sources.
Refer to caption: x4.png

**Table 2: Detailed configuration for different training stages of EMOVA. The table illustrates the vision configurations, dataset characteristics, and training hyperparameters.**
|  | Settings | Stage-1 | Stage-2 | Stage-3 |
| --- | --- | --- | --- | --- |
| Vision | Resolution | 448$\times${{1$\times$2}, $\cdots$, {3$\times$3}} | 448$\times${{1$\times$2}, $\cdots$, {3$\times$3}} | 448$\times${{1$\times$2}, $\cdots$, {3$\times$3}} |
| # Tokens | Max $256\times(1+9)$ | Max $256\times(1+9)$ | Max $256\times(1+9)$ |  |
| Data | Dataset | LCS | EMOVA-Alignment (Fig. 10) | EMOVA-SFT (Fig. 4) |
| # Samples | 558K | 7.4M | 4.4M |  |
| Training | Trainable | Projector | Full Model (Half ViT) | Full Model |
| Batch Size | 256 | 256 | 128 |  |
| LR: $p(\cdot)$ | $1\times 10^{-3}$ | $2\times 10^{-5}$ | $2\times 10^{-5}$ |  |
| LR: $v(\cdot)$ | - | $2\times 10^{-5}$ | $2\times 10^{-6}$ |  |
| LR: $f(\cdot)$ | - | $2\times 10^{-5}$ | $2\times 10^{-5}$ |  |
|  | Epoch | 1 | 1 | 1 |

### 4.3 Overall Training Paradigm

Inspired by Chen et al. (), a three-stage training paradigm is adopted for EMOVA,

- •
Stage-1: Vision-language pre-alignment.
The purpose is to align visual features into the embedding space of LLMs. Only the vision projector $p(\cdot)$ is trained.
- •
Stage-2: Omni-modal text-centric alignment.
This stage jointly performs the vision-language and speech-language alignment simultaneously. We train the LLM $f(\cdot)$, vision projector $p(\cdot)$, and the deeper half of vision encoder $v(\cdot)$ layers.
- •
Stage-3: Omni-modal instruction tuning.
To empower EMOVA to respond accurately to omni-modal instructions, we organize different datasets with various types of instructions to enforce EMOVA to learn generalization across tasks, as detailed in Sec. 5.1.

**Table 3: Comparison on vision-language and speech benchmarks. 1) EMOVA surpasses GPT-4V and Gemini Pro 1.5 on 10 of 14 vision-language benchmarks, while reaching over 95% of GPT-4o performance on nearly all benchmarks. 2) Meanwhile, EMOVA achieves state-of-the-art performance on the ASR task, surpassing its speech counterparts by a significant margin.**
| Benchmarks | EMOVA | Gemini | GPT- | GPT- | LLaVA- | Intern- | Mini- | AnyGPT | VITA |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 8B | Pro 1.5 | 4V | 4o | OV-7B | VL2-8B | Omni | 7B | 8x7B |  |
| MME | 2205 | - | 1927 | 2310 | 1998 | 2215 | - | - | 2097 |
| MMBench | 82.8 | - | 75.0 | 83.4 | 80.8 | 81.7 | - | - | 71.8 |
| SEED-Image | 78.1 | - | 71.6 | 77.1 | - | 75.4 | - | - | - |
| MM-Vet | 55.8 | - | 67.7 | - | 57.5 | 54.3 | - | - | 41.6 |
| RealWorldQA | 64.3 | 68.7 | 61.4 | 75.4 | 66.3 | - | - | - | - |
| TextVQA | 82.0 | 73.5 | 77.4 | - | - | 77.4 | - | - | - |
| ChartQA | 81.8 | 81.3 | 78.5 | 85.7 | 80.0 | 83.3 | - | - | - |
| DocVQA | 90.4 | 86.5 | 88.4 | 92.8 | 87.5 | 91.6 | - | - | - |
| InfoVQA | 64.4 | 72.7 | - | - | 68.8 | 74.8 | - | - | - |
| OCRBench | 824 | - | 656 | 736 | - | 794 | - | - | 678 |
| MathVista | 61.1 | 52.1 | 49.9 | 63.8 | 63.2 | 58.3 | - | - | 44.9 |
| Mathverse | 27.8 | - | 33.6 | - | 26.2 | - | - | - | - |
| ScienceQA-Img | 94.0 | - | 75.7 | - | 96.0 | 97.1 | - | - | - |
| AI2D | 82.8 | 80.3 | 78.2 | 84.6 | - | 83.8 | - | - | 73.1 |
| Librispeech (WER$\downarrow$) | 4.0 | - | - | - | - | - | 4.5 | 8.5 | 8.1 |

## 5 Experiments

### 5.1 Training configuration

#### Stage-1.

In this stage, we only train the parameters of the vision projector $p(\cdot)$ for vision-language pre-alignment with the LCS-558K dataset (Liu et al., ), with the high-resolution image-slicing strategy (Liu et al., ) adopted.

#### Stage-2.

We assemble a unified dataset with 7.4M samples for both the image-text and speech-text alignment, as summarized in Fig. 10.
Specifically, we utilize pre-training datasets from ShareGPT4V (Chen et al., ), ALLaVA (Chen et al., ) (both the original English version and the Chinese version translated on our own), and ShareGPT-4o (Cui et al., ) for general perception, while for
the OCR capabilities, we leverage SynthDog (Kim et al., ), MMC-Alignment (Liu et al., ), K12 Printing, and UReader Text Reading subset (Ye et al., ).
Moreover, we use the 2,000 hours of ASR and TTS data from LibriSpeech (Panayotov et al., ) and AISHELL-2 (Du et al., ) for speech-text alignment, and to preserve the language capabilities of LLMs, we further incorporate the text-only data from Magpie Pro (Xu et al., ).

#### Stage-3.

We collect the EMOVA-SFT dataset consisting of 4.4M multi-task omni-modal samples (see Fig. 4).
We start by gathering high-quality open-sourced visual instruction datasets, including ShareGPT4V (Chen et al., ), InternVL (Chen et al., ), Meteor (Lee et al., ), Idefics-2 (Laurençon et al., ), Cambrian (Tong et al., ), and LLaVA-Onevision (Li et al., ), followed by quality checking, re-formatting all data samples to a unified template, and removing the duplicates.
For speech, we include the training split of EMOVA omni-model instruction data (c.f., Sec. 4.2), with 10% of speech alignment datasets to maintain ASR and TTS performance.
We adopt 128 Ascend 910 Training NPUs in parallel for model training (check more details in Table 2).

### 5.2 Comparison to the SOTA Models

Experimental results are provided in Table 3.
We compare a wide range of state-of-the-art VLLMs, including proprietary ones like Gemini Pro 1.5 (Reid et al., ), GPT-4V (OpenAI, ), GPT-4o (OpenAI, ), and open-sourced ones like LLaVA-OneVision-7B (Li et al., ), InternVL2-8B (Chen et al., ), together with the Speech LLM Mini-Omni (Xie & Wu, ) and omni-modal LLM AnyGPT (Zhan et al., ) and VITA-8x7B (Fu et al., ).

#### Comparison with SOTA VLLMs.

As an omni-modal model, EMOVA achieves comparable performance with state-of-the-art open-sourced VLLMs across multiple benchmarks.
Specifically, our EMOVA outperforms both InternVL2 and LLaVA-OV on MMBench, SEED-Image, TextVQA, and OCRBench consistently, while demonstrating exceptional proficiency in solving math problems requiring precise visual content interpretation.
EMOVA achieves a 2.8% improvement on MathVista compared with InternVL2 and 1.6% higher score on Mathverse compared to LLaVA-OneVision.

Furthermore, EMOVA shows competitive performance even compared with the proprietary models.
EMOVA exceeds both GPT-4V and Gemini Pro 1.5 significantly on 10 out of 14 benchmarks, while for GPT-4o, EMOVA outperforms on both SEEDBench-Image and OCRBench, reaching over 95% of GPT-4o’s performance on ALL evaluated benchmarks except RealWorldQA.

#### Comparison with SOTA omni-modal LLMs.

Compared with VITA-8x7B, EMOVA shows substantial improvement on visual-language benchmarks. Specifically, EMOVA is 112 points higher than VITA on MME, and surpasses VITA by 21.5% on OCRBench (824 vs. 678), underscoring the effectiveness of our approach and the potential to push boundaries of omni-modal abilities.
What’s more, ASR results show that EMOVA significantly outperforms the most recent omni-modal model VITA, even surpassing its SLLM counterpart Mini-Omni, showing the effectiveness of the semantic-acoustic disentanglement and omni-modal mutual benefits.
We further report text-to-speech (TTS) results in Table 6.
See Fig. 5, Fig. 6 and Fig. 8 for qualitative examples. More results can be found in Appendix G.
For the first time, our EMOVA obtains state-of-the-art performance on both the vision-language and speech benchmarks simultaneously.

Figure: Figure 5: EMOVA engages in omni-modal spoken dialogue with structural data understanding.
Refer to caption: x5.png

Figure: Figure 6: EMOVA engages in omni-modal emotional spoken dialogue expressing sadness.
Refer to caption: x6.png

### 5.3 Evaluation of Emotion-Rich Spoken Dialogue

In this section, we evaluate the end-to-end spoken dialogue capabilities of EMOVA. As discussed in Sec. 4.2, the model takes an input image $x_{V}$ and user instructions in the form of speech units $u_{S}$, and outputs text responses, style labels, and corresponding speech units. To ensure comprehensive evaluation, we propose the following evaluation metrics:

- 1.
End-to-end spoken dialogue score assesses the model’s dialogue performance based on the generated speeches, with a score ranging from 0 to 10, reporting the average.
- 2.
Unit-input-text-output score focuses on the quality of the text responses of LLM when the inputs are speech units, bypassing errors from speech synthesis.
- 3.
Text-input-text-output score inputs the ground-truth text of the user instruction and evaluate the model’s text output. This helps disentangle the impact of speech recognition errors and eliminates the influence of the JSON format.
- 4.
ASR and TTS evaluate how accurately EMOVA recognizes speech units and how effectively it generates speech units from text. See Appendix D for more details.
- 5.
Style label classification accuracy evaluates the accuracy of the model in selecting the appropriate speech style labels (Sec. 3.2).
- 6.
Style controllablity assesses the controllability of U2S detokenizer with the given conditional style labels using the confusion matrix that compares the generated and recognized style labels. See Appendix D for more details.

Due to the lack of emotionally rich spoken dialogue evaluation datasets, we split a test set from our synthesized omni-modal instruction-tuning data (see Sec. 4.1).
GPT-4o are used for automated evaluation. Details are provided in Appendix D.

#### Results.

Table 4 the spoken dialogue performance of EMOVA.

(i) By comparing the end-to-end dialogue score with the unit-input-text-output score, we notice that the two scores are closely aligned, with a maximum gap of only $0.22$, except for Speech-Image-ZH.
The TTS-WER/CER is generally low, revealing that EMOVA can synthesize accurate speech based on text responses.
However, the Speech-Image-ZH is an outlier, which we attribute to its complexity.
It includes tasks such as generating poetries and answering riddles, resulting in more intricate responses.
When these answers are converted to speeches and then transcribed back to texts, multiple variations often arise, leading to discrepancies from the original responses.

(ii) Comparing the unit-input-text-output score with the text-input-text-output score, we observe that their differences correlate with the ASR results of speech instructions. Specifically, for Speech-Text-EN and Speech-Text-ZH, which involve more complex instructions,

Figure: Figure 7: Confusion matrix between the generated and recognized emotions. The emotions generated by our U2S detokenizer are recognized with high probability.
Refer to caption: x7.png

EMOVA reports inferior ASR performance ($6.8$ and $12.6$, respectively) compared to other datasets ($2.45$ and $1.85$).
Consequently, when we replace speech instructions with ground-truth transcriptions, EMOVA shows significant improvements from unit-input to text input score.
On the contrary, for datasets with accurate ASR performance, the results are quite similar, suggesting EMOVA retains robust dialogue capabilities when using the JSON format.

(iii) Examining the classification accuracy of style labels, we find that EMOVA performs satisfactorily in classifying emotions and pitch during speech conversations, achieving an accuracy of over 80%. The confusion matrix comparing the conditional and recognized emotion labels is shown in Fig. 7. The results indicate that the four emotions are recognized with high probabilities, with three achieving over 80% accuracy. This demonstrates that our U2S detokenizer effectively controls common emotions, endowing the synthesized speech with vivid emotional expression.

**Table 4: The performance of EMOVA on Speech Dialogue tasks. By default, we evaluate on the corresponding test set of the evaluated datasets.**
| Datasets | End-to-end$\uparrow$ | Text response | Style Categorization | Recognition/Synthesis |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Unit In | Text In | Emotion | Pitch | WER/CER$\downarrow$ | TTS-WER/CER$\downarrow$ |  |  |
| Speech-Image-EN | 7.21 | 7.43 | 7.75 | 89.01 | 98.30 | 2.45 | 4.60 |
| Speech-Text-EN | 6.90 | 7.02 | 8.27 | 87.26 | 90.50 | 6.80 | 6.75 |
| Speech-Image-ZH | 5.87 | 6.51 | 6.44 | 92.60 | 96.60 | 1.85 | 13.95 |
| Speech-Text-ZH | 4.70 | 4.88 | 6.00 | 87.19 | 94.40 | 12.60 | 9.70 |

Figure: Figure 8: EMOVA engages in spoken dialogue with a cheerful tone.
Refer to caption: x8.png

## 6 Conclusion

This work presents EMOVA, an innovative end-to-end omni-modal large language model that effectively aligns vision, speech, and text simultaneously.
We employ a continuous vision encoder to capture fine-grained visual details, while a discrete, semantic-acoustic disentangled speech tokenizer and detokenizer enable end-to-end speech understanding and generation.
A lightweight style module further supports spoken dialogue with vivid emotions.
By using text as a bridge, we demonstrate that omni-modal alignment is achievable without relying on scarce omni-modal image-text-speech data, which not only enhances both vision-language and speech capabilities but also surpasses its bi-modal counterparts through joint optimization.
For the first time, EMOVA achieves state-of-the-art performance on both vision-language and speech benchmarks, setting a novel standard for the omni-modal models for versatile and expressive omni-modal interactions.

## Appendix

## Appendix A More on Speech Tokenizer

### A.1 Speech-to-Unit (S2U) Tokenizer

#### Overview.

To process the speech input $\textbf{X}_{S}$, our S2U tokenizer consists of a speech encoder $s(\cdot)$ with a quantization module $q(\cdot)$.
First, the speech input is passed through $s(\cdot)$, producing a continuous latent representation $\textbf{E}_{S}=s(\textbf{X}_{S})$.
Then, the quantization module $q(\cdot)$ converts $\textbf{E}_{s}$ into discrete units $\textbf{U}_{S}=q(\textbf{E}_{s})$. The final output is an ID sequence $\textbf{U}_{S}=[u_{1},u_{2},\cdots]$, where each $u_{i}$ corresponds to a unique speech unit.

After this S2U extraction process, the speech is represented by quantized IDs instead of quantized embeddings. For example, a speech signal is represented as an ID sequence like $[782,463,550,\cdots]$, which can be treated as a special form of text. As a result, to integrate speech tokens into LLM $f(\cdot)$, we only need to expand the LLM’s original vocabulary $V_{T}$ by adding a set of speech unit tokens $V_{S}$, similar to Zhang et al. (). The expanded vocabulary is thus the union $V=V_{T}\cup V_{S}$.
In this work, the same codebook is shared across multiple languages, such as English and Chinese, enabling the unit modeling abilities to be shared across languages.
This design enables simply integration of the speech modality into the LLM with minimal data requirements (see experiments in Sec. 4.1).

#### Training of S2U.

The training of the S2U encoder involves three primary components: the speech encoder, the quantization module, and the phone decoder.
First, the speech encoder is trained with a large amount of unlabeled speech with contrastive loss in a self-supervised learning manner (Tao et al., ).
The dataset utilized is the 10000-hour English speeches from GigaSpeech (Chen et al., ) and the 10000-hour Chinese speeches from WenetSpeech (Zhang et al., ), both of which encode large variations in speakers, styles, and acoustic environments.
Subsequently, the speech encoder, along with the quantization module and phone decoder, are optimized on a small amount of speech-text pair data, which is derived from the train-clean-100 subset of LibriSpeech (Panayotov et al., ) in English and AISHELL-1 (Bu et al., ) in Chinese. The phone label is obtained using the phone set in Charsiu (Zhu et al., ). During training, the speech encoder encodes input speeches into continuous latent representations that are rich in both phonetic and tonal information. Then, the quantization module is used to convert the continuous outputs from the speech encoder into discrete units. Finally, the phone decoder converts the quantized embeddings into a sequence of non-tonal/tonal phones, ensuring that the speech units capture necessary information related to semantic contents in both non-tonal and tonal languages.
After training, only the speech encoder and the quantization module are used in EMOVA.

#### Comparisons with SpeechTokenizer in AnyGPT.

Our S2U tokenizer differs from the SpeechTokenizer (Zhang et al., ) used in AnyGPT (Zhan et al., ), in the following aspects:

(1) SpeechTokenizer encodes both semantic contents and acoustic details of speeches, while our S2U tokenizer focuses solely on semantic contents. This design reduces the LLMs’ burden of disentangling different aspects of speech information, facilitating the modality alignment between speech and text modalities during LLM training.

(2) Compared with SpeechTokenizer, our S2U tokenizer offers a more concise representation and helps to simplify and accelerate the generation of EMOVA. SpeechTokenizer employs tokens from eight RVQ layers with a 50Hz frame rate to represent speech, thus a 10-second speech corresponds to 500 × 8 = 4000 tokens. However, we reduce the frame rate from 50Hz to 25Hz and utilize only one token to represent each frame, and thus, a 10-second speech can be represented by only 250 tokens. Moreover, AnyGPT requires a two-stage generation process, involving autoregressive (AR) semantic token generation followed by the non-autoregressive (NAR) acoustic token generation. Instead, we only need to generate speech units capturing semantic contents in a fully AR manner.

(3) SpeechTokenizer lacks an explicit structure design to deal with tonal languages like Chinese, therefore, the processing ability in Chinese is not demonstrated in either SpeechTokenizer or AnyGPT. In contrast, our S2U tokenizer incorporates training constraints to better capture tone variation in phone, making it effective for both the non-tonal and tonal languages. This further enhances EMOVA’s multilingual speech processing capabilities, enabling it to effectively handle both English and Chinese.

In summary, our S2U tokenizer improves the compactness and generality of speech representation, facilitates LLM training, and enhances its multilingual speech ability. Experimental results show that our model significantly outperforms AnyGPT in ASR tasks, as shown in Table 6.

Figure: Figure 9: U2S detokenizer with style control.
Refer to caption: x9.png

### A.2 Unit-to-Speech (U2S) Detokenizer with Style Control

#### Overview.

The LLM, along with the vision encoder and speech tokenizer, is trained end-to-end to generate responses in the form of the speech units, given the input images and speeches. Specifically, the output speech units can be obtained via $\textbf{U}_{S}^{o}=f(\textbf{U}_{T},\textbf{U}_{S},\textbf{H}_{V})$, followed by a U2S detokenizer to convert the discrete speech units $\textbf{U}_{S}^{o}$ into the final output speech waveforms.

The proposed U2S detokenizer involves three core modules: the speech unit encoder $e(\cdot)$, the speech style encoder $g(\cdot)$, and the speech decoder $d(\cdot,\cdot)$.
First, the speech unit encoder converts the speech units $\textbf{U}_{S}^{o}$ into unit embeddings $\textbf{E}_{unit}^{o}$.
Meanwhile, the style encoder $g(\cdot)$, adopting the structure of Meta-StyleSpeech (Min et al., ), is utilized to extract a style embedding $\textbf{E}_{style}^{o}$ from the chosen reference speech.
Lastly, the speech decoder $d(\cdot,\cdot)$ reconstructs the speech waveform $\textbf{Y}_{S}^{o}$ from the unit embedding $\textbf{E}_{unit}^{o}$ and style embedding $\textbf{E}_{style}^{o}$.

#### Training of U2S.

Training a U2S detokenizer with emotion controls is challenging considering the lack of labeled emotional speech data since most open-source speech data is predominantly neutral in emotion or lacks emotion labels.
Due to the limited availability of emotion-rich data, we utilize TTS tools (Du et al., ) to generate a small set of style-rich speech samples diverse in speaker identities, genders, emotions, and pitches.
Our U2S detokenizer is first pre-trained on LibriTTS (Zen et al., ) and AISHELL-1 (Bu et al., ) to acquire fundamental speech synthesis capabilities, and subsequently, the synthesized style-rich speech data is utilized to fine-tune the U2S detokenizer, enhancing its controllability over diverse speech styles.

#### Style Prototypes.

To better facilitate controls of genders, emotions, and pitches, inspired by Min et al. () that a small number of style reference speeches can effectively transfer the target styles, we adopt a “store-for-usage” manner, i.e., we construct a style prototype codebook in advance for speech style assignation.
Specifically, we synthesize $K$ reference candidates with external TTS tools for each possible combination of the following styles: two genders (male, female), seven emotions (neutral, happy, sad, angry), and three pitches (normal, high, low), leading to 24 unique styles and $24\times K$ candidates. Empirically we find that genders and pitches are easy to control using any of the candidate references, while the emotion intensity varies across speeches.
To tackle this, we adopt Emotion2Vec (Ma et al., ), a powerful speech emotion recognition (SER) tool, to measure the emotion intensity of each candidate reference, and rank them in terms of the confidence of the desired emotion. We select the Top-1 candidate reference in each combination style to be the prototype of this condition. Finally, the most representative 24 reference speeches are selected from the $24\times K$ candidates.

## Appendix B More on Omni-modality

### B.1 Omni-modal Text-Centric Alignment

#### Modality alignment data

is summarized in Fig. 10.

#### Experiments on Omni-modal Alignment Paradigms.

The training configuration adopted in Sec. 4.1 is mostly identical to Table except that we use a unique resolution of 448 for all stages and replace EMOVA-SFT in Stage-3 with ShareGPT4V (Chen et al., ) for efficiency.

Given the space constraints, the evaluation benchmarks in Fig. 2 represent selected benchmarks from each category in Table 3. Specifically, for general image perception and understanding, we choose MMBench and MM-Vet; for mathematical problem solving, we adopt MathVista (testmini); for science understanding, we select ScienceQA-Img; and for automatic speech recognition (ASR), we utilize the test-clean split of the LibriSpeech dataset.

### B.2 Omni-modal Instruction Data Synthesis

#### Dataset construction.

To obtain emotion and pitch labels, we leverage GPT-4o using the prompt in Fig. 22.
Table 5 shows the distribution of speech styles of our speech instruction dataset.

Figure: Figure 10: Overview of EMOVA omni-modal alignment data composition.
Refer to caption: x10.png

#### Detailed data organization.

As discussed in Sec. 4.2, the omni-modal instruction data is formulated as $D_{\text{omni}}=\{(x_{V},u_{S},x^{o}_{T},c^{o}_{\text{style}},u^{o}_{S})_{i}\}
_{i=1}^{N}$. In details, the textual outputs $x_{T}^{o}=(x_{T}^{o^{1}},x_{T}^{o^{1}})$ contain the transcribed textual instructions $x_{T}^{o^{1}}$ and the textual responses $x_{T}^{o^{2}}$.
The styles labels $c^{o}_{\text{style}}=(c^{o}_{\text{emo}},c^{o}_{\text{p}})$ include the emotion and pitch labels, respectively.

#### Mathematical formulation of chain of modality.

Based on the notations above, the sequential chain of modality approach can be mathematically formulated by decomposing the conditional likelihood of the desired outputs $(x_{T}^{o^{1}},x_{T}^{o^{1}},c^{o}_{\text{emo}},c^{o}_{\text{p}},u^{o}_{S})$ given the inputs $(x_{V},u_{S})$. Specifically, let $z_{1}=x_{T}^{o^{1}},z_{2}=x_{T}^{o^{1}},z_{3}=c^{o}_{\text{emo}},z_{4}=c^{o}_{
\text{p}},\text{and},z_{5}=u^{o}_{S}$, the decomposition is expressed as:

$$ $\mathbb{P}(x_{T}^{o^{1}},x_{T}^{o^{1}},c^{o}_{\text{emo}},c^{o}_{\text{p}},u^{ o}_{S}\mid x_{V},u_{S})=\prod_{i=1}^{5}\mathbb{P}(z_{i}\mid z_{1:i-1},x_{V},u_ {S}).$ (2) $$

**Table 5: Statistics of the EMOVA speech instruction tuning datasets.**
| Dataset | Source | # Examples | Emotions | Pitches |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Neutral | Happy | Sad | Angry | Normal | Low | High |  |  |  |
| Speech-Image<br>-EN(train) | ALLAVA | 71,474 | 58,506 | 12,412 | 516 | 40 | 70,962 | 392 | 120 |
| Speech-Image<br>-EN(test) | ALLAVA | 1,056 | 434 | 274 | 300 | 48 | 44 | 176 | 16 |
| Speech-Image<br>-ZH(train) | ALLAVA<br>(ZH) | 38,260 | 29,893 | 7,680 | 607 | 80 | 36,363 | 624 | 1,273 |
| Speech-Image<br>-ZH(test) | ALLAVA<br>(ZH) | 616 | 96 | 193 | 190 | 137 | 381 | 177 | 58 |
| Speech-Text<br>-EN(train) | ShareGPT | 73,658 | 42,334 | 20,946 | 4,674 | 5,704 | 60,352 | 5,518 | 7,788 |
| Speech-Text<br>-EN(test) | ShareGPT | 1,400 | 200 | 400 | 400 | 400 | 582 | 422 | 422 |
| Speech-Text<br>-ZH(train) | In-house | 47,936 | 29,769 | 16,405 | 1,446 | 316 | 42,356 | 4,379 | 4,379 |
| Speech-Text<br>-ZH(test) | In-house | 686 | 96 | 196 | 198 | 196 | 458 | 134 | 92 |

## Appendix C More on Benchmark Evaluation

To thoroughly evaluate our model’s vision-language abilities, 14 benchmarks covering four different aspects of real-life scenarios are utilized for a comprehensive assessment across multiple domains.
Moreover, Automatic Speech Recognition (ASR) and Text-to-speech (TTS) are adopted to evaluate speech-language abilities.

#### Document/chart understanding and OCR abilities.

Benchmarks including the TextVQA (Singh et al., ), ChartQA (Masry et al., ), DocVQA (Mathew et al., ), InfoVQA (Mathew et al., ), and OCRBench (Liu et al., ), focus on recognition and understanding of structured data (e.g., charts, documents, and characters), challenging the model to extract, comprehend, and reason with structural and textual data.
We adopt the corresponding test set for evaluation.

#### General image perception and understanding.

MME (Fu et al., ), MMBench (Liu et al., ), SEED-Bench-Image (Li et al., ), MM-Vet (Yu et al., ), and RealWorldQA (xAI, ) evaluate models on perception and reasoning among general vision domains,
providing a comprehensive measurement of models’ real-world generalization abilities.

#### Mathematical problem solving.

MathVista (testmini) (Lu et al., ) and MathVerse (vision-mini) (Zhang et al., ) evaluate the model’s ability to deal with diverse mathematical problems, including both arithmetic and complex reasoning questions across different levels of complexity.

#### Science understanding.

ScienceQA-Img (Lu et al., ) and AI2D (Kembhavi et al., ) are utilized to assess the model’s ability to deal with scientific questions and diagrams, which requires reasoning across various subjects and interpreting structured visual content.

#### Automatic speech recognition (ASR).

We utilize the test-clean set of LibriSpeech (Panayotov et al., ) for English, reporting the Word Error Rate (WER) as the evaluation metric. For Chinese, evaluation is conducted on the test set of AISHELL-2 (Du et al., ), using the Character Error Rate (CER). Both WER and CER assess ASR performance, calculated by comparing the recognized texts with the ground-truth transcripts.

#### Text-to-speech (TTS).

To evaluate the TTS abilities, we first prompt EMOVA to generate speech units, which are then converted to speech waveforms by the U2S detokenizer. Using the synthesized speech as input, we conduct ASR with Whisper-large-v3 and Paraformer-zh for English and Chinese, respectively, to obtain transcribed texts. We then compute the WER and CER between the ground truth texts and the transcribed texts as metrics for TTS. The resulting metrics are denoted as TTS-WER and TTS-CER for English and Chines.

## Appendix D More on Evaluation of Speech-Language Capabilities

### D.1 Calculation of Evaluation Metrics

#### End-to-end spoken dialogue score.

We prompt GPT-4o with the original question $x_{T}^{o^{1}}$, the ground-truth text answer $x_{T}^{o^{2}}$ and the transcribed text from the generated speech, to obtain a score ranging from 0 to 10 and report an average of them. The prompt can be found in Fig. 24.

#### Unit-input-text-output score.

Similar to end-to-end spoken dialogue score, but we use the predicted text response $\tilde{x_{T}^{o^{2}}}$ as answer instead of the transcribed text from the generated speech, to obtain a score ranging from 0 to 10 and report an average of them. See the prompt in Fig. 23.

#### Text-input-text-output score.

The prompt can be found in Fig. 23.

#### Style label classification accuracy.

We use GPT-4o to decide whether the style predictions $\tilde{c_{\text{emo}}^{o}},\tilde{c_{\text{p}}^{o}}$ are correct given the transcribed instruction $\tilde{x_{T}^{o^{1}}}$ and the predicted text response $\tilde{x_{T}^{o^{2}}}$. The prompt can be found in Fig. 25.

#### Emotion controllablity

of our U2S detokenizer is assessed by providing texts to LLM to generate corresponding units (i.e., TTS), which, along with the given conditional emotion labels, are then fed into our U2S detokenizer to synthesize speech.
We choose female voice due to its large variation of styles. We evaluate on 4 commonly-seen emotion, i.e.,“neutral”, “happy”, “sad”, and “angry”. We synthesize 200 speech utterances for testing, with 50 utterances per emotion.
The output speeches are analyzed by a Speech Emotion Recognition (SER) model named Emotion2Vec (Ma et al., ), which identifies the emotion with the greatest likelihood among these four emotion.

Table: Table 6: Comparison on the ASR and TTS benchmarks.

### D.2 Comparison with other Omni Models

Experimental results of ASR and TTS are reported in Table 3 and 6.
EMOVA achieves significant improvements over other omni-modal models (i.e., AnyGPT and VITA), even surpassing its SLLM counterpart Mini-Omni (Xie & Wu, ), demonstrating the effectiveness of semantic-acoustic disentanglement and omni-modal mutual benefits.
For the first time, our EMOVA obtains state-of-the-art performance on both the vision-language and speech benchmarks simultaneously.

## Appendix E More on Vision-language

**Table 7: Ablation on the ViT configurations and templates for vision-language alignment.**
| ViT | ViT LR | Template | MME | MMBench | SEED-Image | TextVQA | ChartQA | DocVQA | InfoVQA | OCRBench | ScienceQA-Img | AI2D |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Full | 2$\times 10^{-6}$ | QA | 1928 | 68.8 | 72.5 | 64.3 | 29.9 | 45.2 | 28.7 | 495 | 76.3 | 61.8 |
| Half | 2$\times 10^{-6}$ | QA | 1838 | 71.3 | 72.8 | 63.3 | 31.4 | 46.0 | 28.5 | 489 | 76.1 | 63.7 |
| Frozen | 2$\times 10^{-6}$ | QA | 1887 | 68.8 | 72.2 | 61.3 | 30.2 | 44.7 | 28.0 | 478 | 75.9 | 62.8 |
| Half | 2$\times 10^{-5}$ | QA | 1833 | 68.3 | 73.1 | 62.2 | 27.8 | 42.4 | 27.3 | 461 | 74.7 | 63.4 |
| Half | 2$\times 10^{-6}$ | Plain | 1909 | 70.1 | 72.0 | 61.5 | 24.5 | 38.9 | 30.1 | 410 | 77.0 | 63.6 |

This section analyzes the pre-training configurations of the vision encoder and the prompt template during Stage 2, as shown in Table. 7. Our final selection is the colored setting. We find that training the ViT model with half of the deeper parameters (Chen et al., ) with a learning rate of 2$\times 10^{-6}$ (Liu et al., ) yields the best performance. Furthermore, we compare the QA template with the plain template (Liu et al., ) and find that the QA template is superior for pre-training.

## Appendix F Limitations

#### Duplex modeling.

In the current version, EMOVA can only process either visual/speech/text inputs or produce speech/text outputs at the same time. For a communication experience that mirrors human interaction, handling inputs and outputs simultaneously is crucial.
Recent works like VITA (Fu et al., ) and LSLM (Ma et al., ) have begun to explore duplex modeling.
VITA focuses on recognizing speech in noisy environments during the generation process to facilitate timely responses. LSLM, on the other hand, attempts to halt speech production when it detects a command or voice. Recently, a ground-breaking work named Moshi (Défossez et al., ) develops a model supporting fully duplex modeling. The adeptness at simultaneously managing the information streams from both the user and the assistant allows Moshi to converse with human beings in real-time scenarios.

However, incorporating emotion into this duplex modeling presents additional challenges. Emotional states can fluctuate throughout a conversation, and how to generate appropriate responses given the user’s previous and current emotional cues has not been thoroughly investigated. We will dive into this topic in the future work.

#### Direct unit-to-unit generation.

Although speech units have served as speech representation, they are predominantly adopted in conjunction with text-based assistance (Zhang et al., ).
However, the direct generation from unit to unit without text assistance is an area that has not been extensively explored.
In Lee et al. (), speeches from the source language are directly translated into speech units of the target language for speech-to-speech translation. Similarly, Nguyen et al. () builds a language model directly on speech units, enabling spoken dialogue generation from raw audio. Both works develop models in speech-only data.

In the current version of EMOVA, the text modality is integrated into the speech generation process to transfer textual knowledge to the speech modality, thereby enhancing the correctness of speech responses. In the future, we will strengthen the model’s direct unit-to-unit generation capabilities to boost the speed of speech generation and augment the model’s comprehension of speech modality.

#### Vision configurations.

Currently, we only utilize a single vision encoder pre-trained via a vision-language manner, while recent works have shown effectiveness by combining vision encoders pre-trained by different manners (e.g., self-supervised pre-training (Liu et al., )) and architectures (e.g., MoE (Zhili et al., ; Liu et al., )).
We prioritize visual understanding in this work, while the incorporation of (controllable) visual generation (Chen et al., ; Gao et al., ; Li et al., ; Wang et al., ; Liu et al., ; Gao et al., ) is also appealing to better empower EMOVA in real-life applications (Li et al., ; ; Han et al., ).

## Appendix G Qualitative Results

#### Advanced Vision-language Abilities.

EMOVA exhibit advanced vision-language abilities in humor understanding (Fig. 11), numerical calculations (Fig. 12), coding (Fig. 13), geometry problem solving (Fig. 15).

#### (Omni-modal) Emotional Spoken Dialogue.

EMOVA can engage in omni-modal emotional spoken dialogue (Figs. 6, 8, 14, 17). For demonstration purposes, we present only the speech transcriptions(^2^22For speech files, please refer to our project page.) of the instruction and EMOVA’s response. The text highlighted in blue indicates the style labels predicted by EMOVA, reflecting the emotion and pitch of the generated speech.

#### Omni-modal Spoken Dialogue with Structural Data Understanding.

EMOVA can perform structural data understanding in spoken dialogue. The data types include PDF files (Fig. 5), movie posters (Figs. 16 and 18), personal résumés (Fig. 19), charts (Fig. 20), and websites (Fig. 21). Remarkably, EMOVA is not specifically trained on spoken dialogue involving these data types. This suggests that our chain of modality data introduced in Sec. 4.2 effectively activates both vision-language abilities and speech understanding acquired during the text-centric alignment stage.

Figure: Figure 11: EMOVA exhibits advanced vision-language abilities in humor sense.
Refer to caption: x11.png

Figure: Figure 12: EMOVA exhibits advanced vision-language abilities in numerical calculations.
Refer to caption: x12.png

Figure: Figure 13: EMOVA exhibits advanced vision-language abilities in coding.
Refer to caption: x13.png

Figure: Figure 14: EMOVA engages in emotional spoken dialogue.
Refer to caption: x14.png

Figure: Figure 15: EMOVA exhibits advanced vision-language abilities in math.
Refer to caption: x15.png

Figure: Figure 16: EMOVA engages in omni-modal spoken dialogue with structural data understanding (i.e., poster).
Refer to caption: x16.png

Figure: Figure 17: EMOVA engages in omni-modal emotional spoken dialogue.
Refer to caption: x17.png

Figure: Figure 18: EMOVA engages in omni-modal spoken dialogue with structural data understanding (i.e., movie posters).
Refer to caption: x18.png

Figure: Figure 19: EMOVA engages in omni-modal spoken dialogue with structural data understanding (i.e., résumé).
Refer to caption: x19.png

Figure: Figure 20: EMOVA engages in omni-modal spoken dialogue with structural data understanding (i.e., chart).
Refer to caption: x20.png

Figure: Figure 21: EMOVA engages in omni-modal spoken dialogue with structural data understanding (i.e., website).
Refer to caption: x21.png

Figure: Figure 22: Prompt used to obtain style labels of the speech instruction dataset.
Refer to caption: x22.png

Figure: Figure 23: Prompt used to obtain Unit-Input-Text-Output Score and Text-Input-Text-Output Score.
Refer to caption: x23.png

Figure: Figure 24: Prompt used to obtain End-to-end Spoken Dialogue Score.
Refer to caption: x24.png

Figure: Figure 25: Prompt used to obtain Classification Accuracy of Style Label.
Refer to caption: x25.png

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 5
presentation: 2
questions: * I would appreciate the authors comments regarding the practicality of adopting a fully end-to-end approach from the viewpoint of improving separate submodules and extending the system capabilities (e.g., to other languages).
* Is the use of gpt-4o for the creation of training labels in compliance with the OpenAI terms of use?
soundness: 3
strengths: The model presented in the paper is a step forward for multimodal language models. The key conclusion, i.e., that training with mixed bi-modal instruction pairs, along with the observation of mutual benefit of the visual and speech modalities, can allow future researchers to extend the multimodal capabilities of LLMs to more diverse modalities.

Regarding specifics, I find the contribution on the speech pipeline with the semantic and style disentanglement of the speech representations especially interesting. Furthermore, I appreciate the  breakdown of the instruction sets used for fine-tuning and the evaluation presented in Figure 4.
summary: The paper  presents EMOVA, a multimodal LLM with  image and speech understanding and controllable speech synthesis capabilities (speaker and emotion). The model is based on LLama 3.1 8B and tuned for speech-language and image-language tasks using a 3-stage pipeline, i.e., tuning of the projection layer, alignment, and SFT. The key observation of this paper is that using bi-modal datasets (speech-text and vision-text) with the text as the central modality is beneficial for both vision-language specific and speech specific tasks, aleviating the need for instructions containing all three modalities. Furthermore, disentanglement of the speech representations into semantic and style components improves results and allows for controllability of the synthetic speech. The model achieves sota results in vision-language and speech benchmarks.
weaknesses: The key negative of this paper is the presentation, which I find a bit cramped. The presentation of key results, intuition and the relation to the literature is good, but the description of the key parts of the architecture (speech tokenizer / detokenizer) and the experimental procedure is delegated into the appendix, calling into question the rigor in the main part of the paper. Also, I find the constant referencing of upcoming sections a bit tedious for the reader.

Secondly, I find the lack of human evaluation for the speech synthesis as an omission. The examples presented in the example page are impressive, but the voice sounds robotic, which calls into question whether the end to end approach is practical in this scenario, or having a separate TTS module which can be independently optimized would be preferable.

Finally, some key information is missing, i.e. whether the model will be released as open-source (and the relevant license) and the amount of compute used for training (what kind of GPUs, how many, for how long). I find this information to be essential when presenting a new model to the community.

---

confidence: 3
contribution: 3
initial_rating: 5
presentation: 4
questions: 1. For the training paradigm, there is a vision-language pre-alignment before omni-modal text-centric alignment. Since in section 4, the observation "image-text and speech-unit-text data benefit each other" is derived, I wonder what will happen if we skip the pre-alignment stage and start with text-centric omni-modal alignment.
2. Since emotional speech is generated by TTS systems, I wonder if the synthesized speech can convey emotion in a natural way like a human.
3. There are two output modes of EMOVA, speech or text. Does the model choose the output modality by itself or the output modality is specified in the system prompt?  As illustrated in Figure 3, when generating speech, the outputs are formatted as JSON. I wonder whether forcing the model to output JSON format when generating speech will harm the performance.
soundness: 3
strengths: 1. EMOVA model has the ability to understand vision, speech and text modalities. Through the introduced text-centric omni-modal alignment, EMOVA maintains a comparable performance on vision-language benchmarks.
2. Integrating LLM with a semantic-acoustic distengled speech tokenizer and Unit-to-speech detokenizer with style control built on VITS, EMOVA can generate speech in different styles.
summary: The paper presents EMOVA (Emotionally Omni-present Voice Assistant), a novel end-to-end, omni-modal language model with the capability to understand three modalities: vision, language, and speech and generate speech with vivid emotion. EMOVA uses a continuous vision encoder and a semantic-acoustic disentangled speech tokenizer for seamless omni-modal alignment and diverse speech style controllability. An efficient text-centric omni-modal alignment is introduced to further improve the vision-language and speech capabilities.
weaknesses: 1. Since only the semantic embedding of speech is quantified to speech units, due to the semantic-acoustic distengled modeling of speech tokenizer, the emotional information in the input speech is not conveyed to the LLM. The model generates emotion labels only based on vision and text modalties, which is not often the case in real dialogue.
2. On vision-language benchmarks, SOTA VLLMs are compared. While on speech benchmarks, only omni-modal LLMs are compared. SOTA SLLMs such as Qwen-Audio and SALMONN are not compared. So the conclusion "EMOVA obtains state-of-the-art performance on speech benchmarks" is doubtful.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 2
questions: According to Section 4.2, the authors define "style" as a broader concept than "emotion". So, only mentioning "emotion" in the paper title and  most descriptions may not be appropriate.
soundness: 2
strengths: 1. The motivation of this study is clear which is to build a multi-modal LLM with the ability to control the style of output speech.
2. A new model architecture and corresponding model training strategies are designed.
3. The proposed method has been evaluated using several vision-language and speech benchmarks.
summary: This paper presents a multi-modal LLM with the ability to control the style of output speech.
weaknesses: 1. The descriptioins of the proposed method are not clear. For example, as the authors mentioned, the semantic-acoustic disentangled speech tokenizer is one of the contributions of this paper. However, the introduction to the disentanglement in Section 3.2 is very simple and it is difficult for readers to get the key ideas.
2. The experimental results are not convincing enough. Only the overal performances of the proposed model on different tasks were evaluated. There are no ablation studeis on the key modules of the proposed method which makes it difficult to accept the contributions of the proposed method. In Section 5.3, there were no comparative models for evaluating the emotion-rich spoken dialogue, and only the synthetic data was used for evaluation.

---

confidence: 4
contribution: 3
initial_rating: 6
presentation: 4
questions: 1. For experiments with GPT-4v and Gemini, Is this comparison fair? Is it possible to test EMOVA's zero-shot ability on some unseen task and show that it outperforms GPT-4v?
2. Where can I find the performance comparison for the synthesis TTS quality? How does it compare to a cascaded system where you have a separate TTS component which takes speech content and style descriptions as input, and EMOVA just generate the text response and then generates a description of how to speak that response out? If there is little difference, then why do we need a full duplex model?
3. Why is the emotion in Fig. 5 useful? If I were to prompt a system to write a poem about an image (which is itself a weird task though), why does reading it with a sad voice better than with a plain voice if I am not instructing it to say it that way?
soundness: 2
strengths: 1. The presentation of the paper is very good. It is easy to follow with clear illustrative figures and plots. The equation notations are also clear and accurate.
2. The proposed EMOVA system contributes to the research community as it is the first single neural network that integrates visual ability into a speech-in-speech-out system and supports speech synthesis with emotions. 
3. The separation of semantic and style tokens is a new application of the existing disentanglement study in the TTS field to the speech-language model research field and experiments have demonstrated the usefulness of this design.
summary: This paper proposes a novel system that supports empathetic interaction with humans via speech as well as being able to understand images. The authors propose to disentangle the speech content and speech style into different embeddings. To achieve a better modality alignment among text, speech and image, the authors propose the omni-modality text-centric alignment using the proposed EMOVA system to train on bi-modal alignment data simultaneously.
weaknesses: 1. The core or central motivation of this paper is a bit unclear to me. Is the motivation to include visual ability in a speech-language model (which is not reflected in its name EMOVA), or to include speech synthesis with emotion in a speech-language model? I agree with the authors that the system is the first to achieve these functionalities in a single system, but I am not sure how those functionalities are connected to be put in a single paper. Why being able to understand an image is crucial to emotional speech synthesis, or why emotional speech synthesis is crucial for understanding an image? Taking the example the authors show, "being able to write a sad poem about the image" and "being able to synthesize sad speech about the image" are not strongly connected in my opinion. Note that this problem also exists in the experiments since none of the test sets used actually reflect the importance of __jointly__ understanding image and interacting in speech with emotions, apart from case studies.
2. Regarding novelty, which is also related to motivation, I need more clarification on which parts of the system haven't been done before. For example, there are full-duplex systems [1,2] that can understand speech and output speech. There exists a lot of work in the TTS community working on disentangling style and text representations for speech synthesis [3,4]. There is work controlling emotion in TTS using natural language prompts [5]. There also exist systems that can understand both speech and visual inputs [6]. I understand that this is the first single speech-LM that achieves all the functionalities above (which I gave credit to in the strength part), but the actual approach for each functionality does not seem to be novel to me. 
3. The experiments showed that EMOVA outperforms GPT-4v on various benchmarks, but most of them are in the training set (hence not zero-shot).

[1]. https://arxiv.org/pdf/2408.02622
[2]. https://arxiv.org/abs/2409.06666
[3]. https://arxiv.org/abs/2306.07691
[4]. https://arxiv.org/abs/1810.07217
[5]. https://arxiv.org/abs/2406.06406
[6]. https://arxiv.org/pdf/2406.15704