# Automatic Speech Recognition Advancements for Indigenous Languages of the Americas
## 2 Related Work  
A limited number of studies focus on machine learning techniques applied to the Indigenous languages of Latin America, most of which focus on corpora analysis, NLP applications [35], machine translation (MT) systems [11; 36], sentimental analysis [37], or just showing the challenges of this task [38]. However, only recent initiatives have aimed to address the challenge of developing ASR systems [39] for the indigenous languagesof America. This issue is shared with other minority languages worldwide, and recent initiatives have been carried out to develop ASR models for the Adi (India) [40] or for the Cook Islands Maori [41] among others. For the languages that concern us in this work, there are no previously reported ASR systems for two of them: Wa'ikhana and Kotiria. In the following paragraph, we summarize the most significant investigations reported in the literature in the area of ASR for Quechua, Bribri, and Guarani. The very first ASR system for the Quechua language was published in 2018, and it was able to recognize spoken numbers from one to ten with an accuracy greater than 90% [42]. It was based on cepstral coefficients (MFCC) features, dynamic time warping (DTW), and k-nearest neighbor (KNN) for classifying the characteristics. Then, the first complete E2E ASR system in Quechua was created, based on a pre-trained model and fine-tuned in a very limited domain of available data; the results were difficult to extrapolate to other domains [43]. The most detailed ASR system of Quechua to date uses the hidden Markov model toolkit (HTK) and evaluates the improvement when using the Gaussian HMM versus the monophonic acoustic model [44]. Later research shows that ASR in Quechua can be improved by adding to the training set's text to speech (TTS) synthetically generated voices [45]. Similarly to Quechua, there are not many reported investigations on ASR in the Guarani language. The very first reported ASR system was based on Gaussian mixture models (GMM), HMM and n-grams [46] and trained and tested with a very limited amount of data. In 2020 and 2021, the OpenASR challenge for low-resource languages included Guarani, increasing the interest in this language [47; 48]. Previous investigations included architectures based on convolutional neural networks (CNN) and factored time delay neural networks (TDNN-F), which significantly improved the previously reported results [49]. A more recent work compared three different pre-trained models fine-tuned with 10 h of labeled Guarani speech data [50]. In 2017, an automatic text-to-voice aligner for the Bribri language, with acoustic models trained on other languages, was published [51]. Initial explorations into ASR focused on Bribri investigated how vowel-tone separation can improve the accuracy of the ASR models when training data are scarce [52]. Finally, in 2023, the first ASR E2E model based on self-supervised architecture for Bribri was reported during the ASRU2023 ML-SUPERB Challenge [53]. Integrating ASR with other language processing techniques, such as word embedding, and adopting a linguistic lens could significantly improve outcomes [54; 55; 56], but this took a significant turn with the advent of self-supervised learning models, such as Wav2 vec2.0 [26; 57]. These models capitalized on the power of contrastive learning to transform raw audio data into valuable speech representations, mitigating the need for extensive transcribed data, a common challenge in Indigenous language contexts. The path forward led to exploring Wav2 vec2.0's potential for low-resource language scenarios [27], and it demonstrated remarkable adaptability and significant potential in addressing these languages [58; 59] or other low-resource domains [60; 61]. More recently, weak supervision emerged as a viable approach for leveraging large-scale, weakly supervised learning to harness the abundant yet imperfectly transcribed public data [62].