Text-to-Speech Software: Hume, ElevenLabs & Resemble
As AI capabilities evolve, text-to-speech (TTS) software is becoming more adept at producing natural, human-like speech.
We evaluated and compared the performance of five different TTS and sentiment analysis tools (Resemble, ElevenLabs, Hume, Azure, and Cartasia) across seven core emotion categories to determine which could most accurately, consistently, and comprehensively recognize emotional tones.
Text-to-speech benchmark results
- Hume (7.40) and ElevenLabs (7.34) achieved the highest overall average scores.
- Cartasia (7.11) showed stable emotional coverage but inconsistent results in some cases (especially for repeated “sad” scenarios).
- Resemble (6.03) and Azure (5.91) performed well on certain emotions but had lower overall averages.
See methodology to learn how we measured and evaluated these tools.
Detailed analysis of text-to-speech software
ElevenLabs
ElevenLabs is an AI voice generator and text-to-speech software focused on expressive, multilingual, and realistic speech synthesis.
Through its Eleven v3 model and wide set of tools, it allows creators and developers to produce human-like audio for storytelling, customer engagement, and digital content.
Developer and API integration
ElevenLabs provides APIs and SDKs for developers to embed AI audio models into their applications. Its Text-to-Speech API, Speech-to-Text API, and Voice Changer API are designed for scalability, low latency, and security.
The system supports over 29 languages and complies with GDPR and SOC II standards, making it suitable for enterprise environments.
Enterprise applications
- Customer service and call centers: Improve AI-driven voice agents for inbound and outbound calls.
- Education technology: Enhance learning tools with conversational AI that supports multiple languages and expressive voices.
- Media creation: Enable content platforms to integrate voice generation, dubbing, and sound effects for professional-quality productions.
- AI assistants: Give digital assistants a voice for realistic and interactive communication.
AI safety and ethics
ElevenLabs emphasizes the responsible use of voice AI. The company implements moderation, accountability, and provenance measures to prevent misuse and ensure ethical AI deployment.
It has also launched initiatives, such as voice ID systems, to protect voice actors and creators from unauthorized replication.
Hume AI
Hume AI is a voice technology company that develops emotionally intelligent AI voice generator systems for creators, developers, and enterprises.
Octave: text-to-speech with emotional understanding
Octave 2 is the latest version of Hume’s text-to-speech engine, described as an omni-capable text and voice model. Unlike conventional TTS systems, Octave understands the meaning and emotional context of language, allowing it to express tone, cadence, and mood naturally.
Octave also supports voice conversion and phoneme editing. Voice conversion enables one voice to be substituted for another while maintaining timing and articulation, which helps with dub or performance adjustments.
Phoneme editing allows precise control over pronunciation and emphasis, supporting custom linguistic fine-tuning.
Applications for creators and enterprises
Hume’s models are used across creative, commercial, and technical industries:
- Audiobooks and podcasts: Enable multi-speaker productions with lifelike emotional delivery.
- Video production: Provide realistic voiceovers and multilingual dubbing.
Developer tools and integration
Hume provides APIs and SDKs for Python, TypeScript, Swift, React, and .NET, enabling integration into various software environments. Developers can access a browser-based playground to test, customize, and deploy voices.
Cartasia
Cartasia’s Sonic-3 is an AI voice generator that combines expressive speech synthesis, contextual understanding, and multilingual capabilities.
Its low-latency performance and secure integration make it suitable for enterprises developing real-time voice agents and conversational systems that require both accuracy and natural communication.
Industry applications
- Healthcare: Provides clear and empathetic voice interaction for patient scheduling and support.
- Customer service: Enhances the user experience with accurate voice responses.
- Gaming: Creates realistic character voices for immersive gameplay.
- Hospitality and logistics: Facilitates booking, tracking, and coordination via natural-language interfaces.
Resemble
Resemble AI is an AI voice generator platform that enables organizations to create, edit, and secure synthetic voices while protecting against deepfake threats.
It is designed for enterprise use, emphasizing both scalability and data security to ensure that voice technologies are safe to implement in real-world environments.
Security and awareness solutions
Resemble also provides AI-based security awareness training to prepare teams for deepfake threats. These simulations replicate real-world attacks through phone, WhatsApp, and email, allowing employees to recognize and respond to fraudulent AI-generated voices. Organizations benefit from continuous monitoring, detailed analytics, and measurable improvements in awareness.
Developer and enterprise use
Developers can integrate Resemble’s features through SDKs and APIs or deploy the system on their own infrastructure. The platform supports multilingual voice generation and can be used for creating conversational agents, virtual characters, and localized speech applications.
Azure
Azure AI Speech is a speech-focused service in Microsoft Azure that helps developers build voice-enabled, multilingual AI applications.
It offers tools for transcribing, generating, and analyzing speech using prebuilt and customizable AI models.
Integration with the Azure ecosystem
Azure AI Speech works with other Azure services:
- Azure OpenAI in Foundry Models integrates multimodal AI that processes text, images, audio, and video.
- Azure AI Content Safety provides tools to monitor and manage responsible AI usage.
- Azure AI Content Understanding converts multimodal data into actionable insights.
Key features explained
Naturalness and voice quality
High-quality text-to-speech software aims to produce human-like speech with accurate prosody and intonation. Minimizing robotic tones is crucial for effective communication in educational, media, and professional contexts.
Voice variety and styles
Modern systems offer multiple voice options and delivery styles, including conversational and formal styles. This variety allows content to be tailored for different audiences and use cases.
Customization controls
Users can adjust speed, pitch, tone, and volume, and insert pauses. Such controls enhance delivery and enable audio output to adapt to a range of settings, from formal presentations to casual listening.
Pronunciation and context sensitivity
Advanced systems account for context to resolve ambiguous words and phrases. Phoneme dictionaries and customizable rules further enhance pronunciation accuracy.
Text normalization
Numbers, dates, abbreviations, and symbols are converted into natural speech. Proper normalization prevents awkward readings and improves listener comprehension.
Exporting and output options
Most software supports saving audio in formats such as MP3 or WAV. Batch processing and real-time streaming are often available to meet both personal and business needs.
Offline or on-device capability
Offline functionality enables speech generation without internet access. This is particularly important for maintaining privacy, supporting low-latency use, and environments with limited connectivity.
Voice cloning and custom voices
Some solutions offer custom voice creation based on speaker samples. This enables personalized experiences but also requires careful consideration of ethical and licensing issues.
Accessibility features
Integration with screen readers, text highlighting, and support for assistive technologies ensures accessibility for users with disabilities. These features are critical for creating inclusive digital environments.
Differentiated features explained
Text-to-speech tools often distinguish themselves through a set of advanced features that extend beyond basic speech synthesis. These features highlight how providers address specific use cases in education, business, media, and accessibility.
Number of languages
The range of supported languages reflects the solution’s adaptability for global users. A larger language library provides a broader reach, making the software suitable for international businesses, universities, and personal use across diverse linguistic contexts.
Voiceover (VO) translation
Voiceover translation enables users to input text or a recorded voice and generate output in a selected language. This feature is crucial in video production, where speech synthesis can replace or supplement the original narration, facilitating multilingual communication.
Video editor
Some providers integrate video editing and creation capabilities into their platforms. This allows subscribers to edit or produce videos while adding speech-based voiceovers directly, eliminating the need for third-party editing tools. The combination of video creation and speech synthesis supports faster content production.
Dubbing
Dubbing extends beyond basic translation by synchronizing the generated audio with the original video’s pacing, expressions, and visual cues. Providers offering this feature ensure that speech pauses, tone, and mouth movements are carefully synchronized, resulting in natural and localized viewing experiences.
Audio editor
An audio editor provides tools for refining synthesized or recorded audio. Adjustments, such as modifying volume, inserting pauses, or applying filters, allow users to achieve professional sound quality without the need for external editing programs.
Subtitles and transcription
In addition to speech synthesis, many providers offer speech recognition features that enable the creation of subtitles or transcriptions. This functionality is the reverse of text-to-speech and is valuable for making content accessible, supporting research, and producing multilingual versions of documents or videos.
Integration and APIs
APIs and SDKs allow speech capabilities to be embedded into applications, websites, and enterprise systems. This integration supports services like chatbots and automated phone systems.
Text-to-speech software use cases
Accessibility and assistive technology
Text-to-speech software plays a crucial role in enhancing accessibility. Individuals with visual impairments or reading disabilities often rely on speech software to access written text in digital formats such as documents, web pages, or PDF files.
By converting text into audible speech, these tools allow users to engage with information that would otherwise be inaccessible. Screen readers and text readers are widely used to read aloud text on websites, research articles, and educational content.
For people with dyslexia or related conditions, hearing content instead of reading helps them focus on meaning rather than struggling with words on a page. Text-to-speech also provides a voice for individuals who have lost their ability to speak.
In such cases, custom voices built from recordings can restore a sense of personal identity and independence.
Education and e-learning
Students often benefit from listening to written text, especially when studying dense academic materials or preparing for exams. Listening can enhance comprehension, reduce fatigue, and enable students to review material while engaging in other activities.
Educational institutions frequently use text readers in e-learning environments, where audio versions of lesson materials help create accessible content for diverse learners. In language learning, the ability to listen to content in multiple languages supports correct pronunciation, rhythm, and tone.
Audio files generated by speech software can be saved and played repeatedly, offering additional features for revision. This allows students at universities and schools to access both text and speech formats, accommodating different learning preferences.
Content creation and media
Content creators are increasingly relying on text-to-speech tools to generate voiceovers for videos, podcasts, advertisements, and training materials. Converting text into audio files allows creators to present information in multiple formats, expanding their reach to audiences who prefer listening over reading.
Authors and publishers also use speech software to convert stories and research into audio versions. This provides accessible content for users who prefer to listen on personal devices while traveling or multitasking.
By utilizing software capable of producing high-quality voices, creators can ensure that their output meets professional standards. Audio formats generated by these tools are compatible with common devices, which makes them practical for both personal use and business purposes.
Customer service and business communication
Businesses utilize text-to-speech software in customer service systems, including automated phone menus, chatbots, and digital assistants. These applications rely on speech to present information clearly and consistently across multiple languages and communication channels. By creating audio from written documents and announcements, companies can ensure that their communication is both efficient and accessible.
Internal business communication also benefits from the ability to convert reports, newsletters, and training materials into audio. Employees can listen to content while managing other tasks, which improves productivity.
Embedded devices and daily use
Text-to-speech technology is now integrated into many everyday devices. Navigation systems in vehicles read aloud directions to drivers, while smart assistants in homes or offices use speech to present reminders and information. Browser extensions and applications can read aloud web pages or documents directly from the screen, enabling users to listen to content while performing other activities.
Individuals also use speech software to convert personal documents, research materials, and study materials into audio files that can be saved and played back on phones, laptops, or other devices later.
Challenges in adopting text-to-speech
Despite the wide range of applications, several challenges limit the effectiveness of text-to-speech systems.
- User awareness: Many users are not fully aware of the additional features that text-to-speech tools provide, such as saving audio, adjusting tone or speed, or creating custom voices. This lack of awareness can prevent users from fully leveraging the available technology.
- Naturalness of speech: Producing speech that conveys human-like emotion, rhythm, and tone remains difficult. Users often expect audio that not only delivers words but also expresses awareness of context and emotion.
- Accuracy of pronunciation: Words, characters, and abbreviations can be mispronounced, especially when converting text across different languages or formats. This can reduce comprehension and quality for international users.
- Format compatibility: While most tools support common audio formats, difficulties may arise when converting complex files that include images, music, or interactive content.
- Performance speed: In real-time applications, such as customer support or live presentations, speech software must generate audio quickly without compromising quality.
- Cost and availability: While some programs are free, software with the highest-quality voices and advanced features is often available only in paid versions, limiting accessibility for students and individuals using these tools for personal use.
Benchmark methodology
Dataset
The dataset used in this evaluation consists of five text files (.txt). Each file contains a single sentence, and each sentence represents one primary emotion: sad, angry, happy, neutral, relaxed, serious, or surprised.
To maintain fairness, the same inputs were provided to all tools, ensuring equal testing conditions.
These sentences were short and derived from real user expressions, meaning they represent natural scenarios commonly encountered in tone and emotion detection. This setup ensures that all tools were tested on realistic emotional content within concise textual inputs.
Evaluation process
The evaluation process involved sending the same five text inputs to each of the five tools.
Each tool produced outputs such as tone of voice, emotion label, and prosody analysis, which were then manually rated on a 0–10 scale based on how well they captured the intended emotion.
- A score of “0” indicates the tool completely failed to detect the intended emotion, while a score of “10” means it perfectly captured it.
- For each of the seven emotions, the average score of each tool was calculated.
- Then, the arithmetic mean of those averages was used to determine the tool’s overall performance score.
- Finally, the results were normalized to ensure a fair comparison between the different tools, accounting for variations in scoring or performance scales.
Evaluation metrics
The evaluation used manual scoring that considered qualitative criteria rather than separate quantitative metrics. When assigning these scores, evaluators considered the following aspects:
- Accuracy: How effectively the tool identified the intended emotion.
- Consistency: Whether the tool’s outputs were similar when processing similar emotional inputs.
- Coverage: How well the tool recognized and distinguished all seven emotion categories.
- Overall impression (average score): A combined judgment of the above three aspects, reflecting the tool’s general performance.
It is noted that these aspects were not treated as separate metrics but were instead collectively considered when assigning each tool’s final manual score, emphasizing a holistic evaluation approach.
Manual scoring was used in this evaluation because none of the available tools could automatically and reliably quantify emotional diversity.
For future work, larger datasets and automated evaluation metrics (such as Precision, Recall, and F1-score) are recommended to provide a more comprehensive benchmark.
Further reading
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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