new challenges in speech recognition
The landscape of automatic speech recognition (ASR) has undergone significant evolution in the past decade. The rise of large end-to-end models capable of learning intricate patterns across languages has led to the assertion that read speech recognition is now a mastered task, particularly in high-resource languages like English, Spanish, or French. This near-perfect recognition of read speech can be attributed to two primary factors: Firstly, read speech is inherently easier to recognize due to characteristics such as single-speaker input, clear articulation, slow rhythm, and optimal recording conditions. Secondly, the abundance of labeled read speech ASR data, predominantly sourced from audiobooks such as the renowned Librispeech dataset, has played a pivotal role in advancing ASR technology.
Despite advancements, speech recognition continues to face considerable challenges in more dynamic scenarios, such as conversational speech or work meetings. Phone calls, street conversations, Zoom meetings, and lectures often involve lengthy and intricate discussions, with multiple speakers employing technical or colloquial vocabulary, speaking rapidly, and overlapping each other. This not only makes recognition difficult but also poses challenges in data collection and labeling. Despite initiatives like the Chime Challenges, the scarcity of data for conversational speech and meetings remains a significant hurdle in developing accurate ASR models, particularly in languages other than English.
meetings
As previously highlighted, meeting scenarios pose significant challenges in speech processing. At Landospeech, we've meticulously gathered and labeled thousands of hours of meetings in multiple languages. Our dataset encompasses a wide spectrum of meeting dynamics, featuring varying speaker counts ranging from 2 to 6 individuals and diverse recording conditions—from virtual Zoom meetings to in-person settings with varied room sizes and characteristics. This comprehensive dataset, available in languages such as English, French, Spanish, Portuguese, and German, empowers you to train models for multilingual meeting speech recognition.
ConversAtions
Conversational speech presents a formidable challenge for contemporary ASR models. With rapid speech rates, casual vocabulary usage, and frequent speaker overlaps, accurately transcribing such dialogue is demanding. Landospeech offers a wealth of labeled conversational data, spanning thousands of hours. This rich resource equips you to train robust ASR models capable of transcribing phone calls and live conversations effectively. Given the scarcity of conversational speech data in foreign languages, Landospeech’s datasets represent a transformative asset in the field.
others
Modern speech recognition systems aspire to excel in recognizing diverse speech recordings across various conditions, including challenging scenarios. Consequently, datasets encompassing read speech, spontaneous speech (e.g., TED Talks), acted speech, and other categories are vital for training robust ASR models. Given the scarcity of data in languages beyond English, Landospeech has amassed extensive volumes of read and spontaneous speech in multiple languages, addressing a significant obstacle in training such models.