Restoring speech communication from neural signals is a central goal of brain-computer interface research, yet EEG-based speech reconstruction remains challenging due to limited spatial resolution, susceptibility to noise, and the absence of temporally aligned acoustic targets in imagined speech. In this study, we propose an EEG-to-Voice paradigm that directly reconstructs speech from non-invasive EEG signals without dynamic time warping (DTW) or explicit temporal alignment. The proposed pipeline generates mel-spectrograms from EEG in an open-loop manner using a subject-specific generator, followed by pretrained vocoder and automatic speech recognition (ASR) modules to synthesize speech waveforms and decode text. Separate generators were trained for spoken speech and imagined speech, and transfer learning-based domain adaptation was applied by pretraining on spoken speech and adapting to imagined speech. A minimal language model-based correction module was optionally applied to correct limited ASR errors while preserving semantic structure. The framework was evaluated under 2 s and 4 s speech conditions using acoustic-level metrics (PCC, RMSE, MCD) and linguistic-level metrics (CER, WER). Stable acoustic reconstruction and comparable linguistic accuracy were observed for both spoken speech and imagined speech. While acoustic similarity decreased for longer utterances, text-level decoding performance was largely preserved, and word-position analysis revealed a mild increase in decoding errors toward later parts of sentences. The language model-based correction consistently reduced CER and WER without introducing semantic distortion. These results demonstrate the feasibility of direct, open-loop EEG-to-Voice reconstruction for spoken speech and imagined speech without explicit temporal alignment.