# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 2 Recent Advances in Speech Recognition  
During the last few years, ASR systems have positively evolved at acoustic modelling with the integration of DNNs in combination with Hidden Markov Models (HMMs) to outperform traditional approaches [8]. More recently, new attempts have been focused on building E2E ASR architectures [9], which directly map the input speech signal to character sequences and therefore greatly simplify training, fine-tuning and inference [10; 11; 12; 13; 14]. Once the potential of the E2E architectures for speech recognition was demonstrated [9], and considering the need to reduce the size and complexity of the ASR models due to their large hardware requirements, new architectures arose to make these models more optimal to be deployed on embedded hardware without loss of quality. More recently, driven by the increasing availability of data in major languages and the scarcity of annotated data in minority languages or specific domains, novel approaches have emerged focused on training big neural models through self-supervised learning methods and making use of hundreds of thousands of unlabelled acoustic data. Nowadays, most of the efforts in the field seem to be focused on this last direction, given the availability of pre-trained models and their high performance when being used as a feature extractor or when they are fine-tuned with in-domain data [15]. Nevertheless, the more conventional hybrid acoustic models also continue to evolve, improving benchmark results over well-known datasets like LibriSpeech [16]. Within the [PERSON] community, a novel neural network architecture was recently presented, known as Multistream CNN [17]. The Multistream CNN acoustic model was inspired in the work presented in [18] but leaving out the multi-headed self-attention layers. This model processes input speech with diverse temporal resolutions by having stream-specific dilation rates to CNNs across multiple streams to achieve the robustness. A factorised time-delay neural network (TDNN-F) is stacked in each stream, while the dilation rate for the TDNN-F layers in each stream is chosen from multiples of 3 of the default sub-sampling rate for both training and decoding. The output embedding vectors from each stream are concatenated and followed by ReLU, batch normalisation and a dropout layer, which is finally projected to the output layer via a couple of fully connected layers. This hybrid model achieved very competitive results on the Librispeech test-clean/other sets in combination with the efficient _self-attentive SRU_[19] language model. Specifically, WER values of 1.75 and 4.46 were reported on the test-clean/other partitions of the Librispeech dataset, respectively [19]. With the aim of building lighter but competitive E2E models for speech recognition, NVIDIA proposed Quartznet [6], based on the main Jasper architecture [20]. This architecture consists of a new E2E neural acoustic model composed of multiple blocks with residual connections in between. Each block consists of one or more modules with 1D time-channel separable convolutional layers, batch normalisation, and ReLU layers. They reached near-SoA error rates on the LibriSpeech dataset, for which WER values of 2.69 and 7.25 were achieved on the test-clean/other sets [6], respectively. These results were reached with the model 15 \(\times\) 5 Quartznet, which contained 18.8 million parameters, in contrast to other larger E2E architectures such as 10 \(\times\) 5 Jasper (333 million) [20], Paddle-Deep-Speech2 (49 million) [9] or Wav2 Vec2.0 (95 to 317 million) [2]. Nowadays, many of the recent works in the ASR field seem to be focused on taking advantage of big acoustic models trained with self-supervised learning methods and a large amount of unlabelled data. Facebook AI demonstrated for the first time that learning powerful representations through self-supervised methods from big amounts of unlabelled speech and then fine-tuning on transcribed speech can enhance neural models trained with semi-supervised techniques [2].